Medicolite-Machine Learning-Based Patient Care Model
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Hindawi
Computational Intelligence and Neuroscience
Volume 2022, Article ID 8109147, 12 pages
https://doi.org/10.1155/2022/8109147
Research Article
Medicolite-Machine Learning-Based Patient Care Model
Rijwan Khan ,1 Akhilesh Kumar Srivastava,2 Mahima Gupta,1 Pallavi Kumari,1
and Santosh Kumar 3
1
Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, U.P. Affiliated to
AKTU Lucknow, U.P., India
2
Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India
3
Department of Mathematics, College of Natural and Applied Sciences, University of Dar es Salaam, Dar es Salaam, Tanzania
Correspondence should be addressed to Santosh Kumar; drsengar2002@gmail.com
Received 11 July 2021; Revised 27 December 2021; Accepted 6 January 2022; Published 25 January 2022
Academic Editor: Elpida Keravnou
Copyright © 2022 Rijwan Khan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper discusses the machine learning effect on healthcare and the development of an application named “Medicolite” in which various
modules have been developed for convenience with health-related problems like issues with diet. It also provides online doctor ap-
pointments from home and medication through the phone. A healthcare system is “Smart” when it can decide on its own and can prescribe
patients life-saving drugs. Machine learning helps in capturing data that are large and contain sensitive information about the patients, so
data security is one of the important aspects of this system. It is a health system that uses trending technologies and mobile internet to
connect people and healthcare institutions to make them aware of their health condition by intelligently responding to their questions. It
perceives information through machine learning and processes this information using cloud computing. With the new technologies, the
system decreases the manual intervention in healthcare. Every single piece of information has been saved in the system and the user can
access it any time. Furthermore, users can take appointments at any time without standing in a queue. In this paper, the authors proposed a
CNN-based classifier. This CNN-based classifier is faster than SVM-based classifier. When these two classifiers are compared based on
training and testing sessions, it has been found that the CNN has taken less time (30 seconds) compared to SVM (58 seconds).
1. Introduction during emergencies. One of the important machine learning
applications is Imaging Diagnosis by various disease de-
The use of digital technology in healthcare has been marked tection like tumour and pneumonia.
by ongoing obstacles in terms of both applicability and Deep learning gives the healthcare business the ability to
practicality. The integration of diverse health systems has examine data at breakneck speeds while maintaining high
been sluggish, and most countries of the world have yet to accuracy. It is neither machine learning nor artificial in-
embrace a completely integrated healthcare system [1]. The telligence; rather, it is a sophisticated hybrid of the two that
inherent characteristics and intricacies of human biology sifts through data at a breakneck speed thanks to a layered
and single-patient variance have continually demonstrated mathematical design.
the necessity of the human element in disease diagnosis and Machine learning has been widely adopted in many
treatment [2]. Advances in digital technology, on the other industries, including healthcare, thanks to advancements in
hand, are unquestionably becoming vital tools for healthcare data technologies such as storage size, computational
professionals in giving the best possible treatment to their power, and data transit speeds. Recent medical develop-
patients. Through advanced technologies, new solutions ments have stressed the requirement for a customised
come to make meaningful changes [3, 4]. Apart from doctor medication addressed to healthcare, due to the multidi-
appointments, it also makes the human task easy for mensional nature of providing effective healthcare to an
searching nearest hospital by tracking the location through individual [6]. Figure 1 shows the benefits of different big-
the GPS [5]. It also makes patients suffer less due to pain data technologies in the healthcare field. The purpose of
2 Computational Intelligence and Neuroscience
Nursing Homes Security
Specialists Smart Healthcare Integration
Management Information
Remote
Patient care
Monitoring
Figure 1: Smart healthcare management system.
personalised medicine is to uncover, anticipate, and analyse appointment to diagnosis. In this paper, the authors im-
diagnostic decisions using enormous volumes of healthcare plement their system on a dataset of Brain Tumour and Lung
data, which physicians may then implement for each in- Infections with Artificial Intelligence and Machine Learning
dividual patient. The first is the use of machine learning in algorithms. This system is based on the CNN-based system
medical pictures such as Magnetic Resonance Imaging and this CNN-based system is compared with all other
(MRI), Computerized Axial Tomography (CAT) scans, systems, that are shown in Table 1.
ultrasound imaging, and Positron Emission Tomography
(PET) scans [1, 6]. The end result of these imaging mo- 2. Literature Survey
dalities is a collection or series of images that must be
interpreted and diagnosed by a radiologist [2, 6]. Machine Researchers have developed various machine learning algo-
learning algorithms have advanced fast in their ability to rithms in recent years that modify the input data distribution
anticipate and locate photos that may signal a disease state to generate inaccurate classification in the output [12]. Most of
or a major concern. them are aimed at modifying the distribution of input data so
This system provides mainly three services, viz, services that the training data can be altered and the output classified.
for patients, services by hospital staff, and services by ad- With the development of eHealth, our involvement in the
ministration. Patients can check their history or records and healthcare industry has grown [1, 12]. A scholar used data
can make an appointment with the doctor. There are doc- from Beijing Tongren Hospital in China to develop a deep
tor’s ID numbers and qualifications by which patients can learning model that uses the CNN for glaucoma diagnosis. The
take appointments. In the administrative system, there is collection contains 3554 photos of the retinal fundus taken
Human Resources (HR) which checks all fee-related in- from 2000 patients with a variety of eye disorders. There are
formation of a particular patient. Modern Healthcare 1391 photos in this dataset that have been identified with
changes the lives of human beings positively. glaucoma, while the remaining images (2163) have not. A
This paper is organized in 5 main parts: (i) Literature paradigm for diagnosing bacterial sepsis in severely unwell
Survey, (ii) Problem Statement, (iii) Key Technologies to Find patients was proposed by another scholar. The information
the Solutions, (iv) Comparison with Other Systems, and (v) was gathered from the Medical ICU (MICU) and ICU de-
Result Analysis and Conclusion. In the literature survey, authors partments of the Guangzhou General Hospital (185 inpa-
discussed the previous development of the Smart Medicare tients). The proposed model has a 90.8 percent accuracy. ML is
system. In the problem statement, first authors find the gap in an informatics area that evolved from the recognition of
the previously developed tool and then discuss their own patterns and the idea of computer learning [11]. It is a form of
problem statement. After that, it discussed some key technol- artificial intelligence (AI) that gives machines the ability to
ogies that are used to find the smart solution in Medicare. These learn through difficult decisions, without explicit program-
technologies are Machine Learning, Deep Learning, Con- ming [2, 12]. The applications of computer vision [2, 11],
volutional Neural Network, Image Processing, and Transfer computer graphics [12], natural language processing (NLP)
Learning. This system is compared with an already developed [11], speech recognition [13], and computer networks have
system and finds that our system is more efficient and powerful been effective in recent years. Existing techniques are inef-
compared to other systems; authors discussed the result in the fective because they are less accurate and because they con-
result analysis part. Data were collected using Kaggle to create sume more energy that does not provide for these different
the medical imaging and approximately 4700 image slices were applications. To meet different applications, these strategies
taken. It has data for 50 patients. need to be improved. In IoT eHealth, ML approaches are
In this system, the authors developed a tool which helps crucial [1, 13]. It enables us to acquire profound analysis from
patients and doctors for Smart Medicare. The parts of smart a wide range of information available. Millions of sensors are
management are Nursing Homes, Specialists, Remote attached with different physiology, environmental, and
Monitoring, Security, Integration Information, and Patient behavioural factors to patients that continuously control their
Care. It has two main parts, one is the Patient Management health. During testing, different models in ML are subjected to
System and the other is the Doctor Management System. small changes in the input data, which can then be used to
These systems are explained in detail in Figures 2 and 3. The affect the distribution of the data by enforcing poisoned test
whole system is a machine learning-based system from data [2, 13], for example, neural networks, deep neural
Computational Intelligence and Neuroscience 3
Brain Tumor
Detection
Blood Request BMI Test
t
ag nt
Search nearby
en
an tie
Prescription
em
hospitals
M Pa
History Appointments
Pneumonia
Detection
Figure 2: Patient management system.
View patient
information
Scheme Track/Schedule
Appointment appointment
an in r’s
en t
Send Reminders Assign time slot
em en
M ppo cto
ag tm
t
to patienets to each doctor
A Do
Appointments Check doctor’s
cancellation availability
Save No-show
appointment
information
Figure 3: Doctor management system.
Table 1: Comparison with different techniques. Our new Smart Healthcare model, “Medicolite,” takes a
completely different approach to the traditional ML-based
S. Accuracy Specificity Smart Healthcare System (SHS) [14]. While most studies
References Algorithm
No. (%) (%) look at single medical device data or stored healthcare data
[7] 1. KNN 75 72 to apply adversarial attacks, we look at a networked mul-
[8] 2. SVM 86 78 tidevice healthcare system that uses machine learning
[9] 3. Naı̈ve Bayes 83 86
models to identify disease and normal behaviour [15]. The
[10] 4. Random Forest 80 65
Logistic
following are the primary differences between the existing
[11] 5. 81 84 method and our fresh new method:
Regression
(i) The majority of previous research has focused on
modifying an ML model’s data distribution, not in
networks, etc. The main purpose to change the forecast disease online mode [8]. Authors propose a real-time ML-
in ML systems is to modify the distribution of data in cross- based Smart Healthcare System in which authors
ML classifiers [14]. alter the input data distribution of several medical
4 Computational Intelligence and Neuroscience
instruments to perform diagnosis and disease de- other emerging technologies help doctors, patients, medical
tection using Convolutional Neural Networks staff, nurses, emergency medical services, and radiologists.
(CNNs). Figure 4 shows the proposed system that will also describe
(ii) Authors discover the smallest number of compro- how to use current advances in machine learning to auto-
mised medical devices in an SHS, whereas the mate the entire healthcare system, which has never been
previous method focuses on generating multilabel done before in existing studies. In this proposed system,
discrete EHR [16]. there are two main modules which are the Patient Man-
agement System and Doctor Management System. The user
For individuals and diseases with specific characteristics, can be anyone, either a patient or a doctor. There are dif-
the dataset in the present system is generally limited. These ferent log-in page available for both patients and doctors.
technologies are primarily intended for the treatment of There are separate features in both Doctor and Patient
more massive illnesses like heart disease and cancer [8]. modules. In Patient modules, there are different features
Changes in illness and its affecting variables may not always available like disease detection (brain tumour and pneu-
match the preselected criteria, resulting in inaccurate monia), BMI test, prescription, and patients can also take
findings [2, 8]. Disease symptoms change throughout time appointments according to their requirements. This system
since we live in an environment that is always changing also suggests nearby hospitals to patients in any emergency.
[4, 8]. In addition, most existing systems make users wait In the Doctor module, a doctor can take an appointment and
extended periods of time by requiring them to complete can also cancel it if they are busy with another appointment.
extensive questionnaires [16]. The authors propose a system Another feature includes prescribed medicine according to
with a user interface that is simple and elegant, as well as patient health. The smart healthcare system is called “Smart”
efficient in terms of time. We are looking for a more precise because it has less manual work and uses the latest tech-
questionnaire that the system will follow to make it less time nologies like machine learning and suggests the nearest
demanding [16]. Many systems’ current strategy totally hospital which is an essential task in an emergency. Patients
depends on automating this process, which falls short of can take appointments at any time with any specialist.
establishing the system’s user’s confidence. The next sections explain how technologies help various
actors and how the proposed system works.
3. Problem with Existing System
Communications are also critical for a smart healthcare 4.1. Patient Management System. This smart healthcare
system, according to existing systems. Due to appointments system allows patients to be monitored at home without
and a lot of manual effort, it takes very hard work and time to having to wait in a big line, and it can track their day-to-day
organize and maintain appointments and records in several activities. It helps the operator provide accurate information to
existing system models [17], whereas it is inflexible to plan patients and responds to all patient requests quickly. By
and maintain appointments and records in a smart providing powerful search tools and excellent features, it
healthcare system. The former technique did not provide improves the level of service given to users. Varied modules in
information on the availability of physicians, nurses, and the Medicolite provide patients different benefits. The most
other people to executives. The existing models do not depict essential role that machine learning will play for patients is that
hospital locations, causing patients to suffer as a result of the it will offer all relevant patient data on time and will appro-
long journey. In manual labour, records are occasionally priately screen the affected area of the patient. ML counsels the
lost, and the patient’s medical history is unavailable, causing patient and assists the physician in providing appropriate care.
them to suffer. Several prior studies have demonstrated that These machine learning-based support systems are sometimes
cloud storage capable of storing large volumes of varied data smarter than doctors, able to make accurate diagnoses, and
is critical to a big-data healthcare system [17]. Because no treat patients with critical illnesses more effectively than
diagnosis can be made without human intervention, the doctors. Through this system, a patient can use GPS tracking
patient must wait for appointments. Many recent academics to find a local hospital and alleviate pain. It also saves the
have noticed the potential of Machine Learning and Cloud history of the conversations between the patient and the
Computing as a solution for healthcare as an outcome is one doctor. Figure 2 shows the Patient Management System and
of the many advantages of smart health monitoring [18]. ML the specific features provided by the application which consists
healthcare systems have been designed for specialized goals of mainly disease detection (brain tumour and pneumonia),
in numerous works, including rehabilitation, diabetes request for any blood group, history of doctor appointments,
management, tumour diagnostics, and more. Even though and search hospital which is nearby in rush.
these systems were created for a variety of reasons, they are
all linked by the employment of comparable enabling 4.2. Doctor Management System. This system eases the work
technologies. of doctors by providing appointments online and diagnoses
patients through the app, assign tests and treatments, and
4. Proposed System create prescriptions for them. In addition, the doctor may
keep track of the patient’s symptoms, allergies, and drug
The proposed architecture for machine learning assisted recommendations. Figure 3 shows the various features in the
smart healthcare systems, in which machine learning and doctor module like schedule patient appointmentsComputational Intelligence and Neuroscience 5
Medicolite
Modules
Patient Doctor
Management Management
System System
(i) Brain Tumor Detection (i) Accepted or rejected the
(ii) BMI Test appointment
(iii) Search nearby hospital (ii) Prescribe the medicine
(iv) Pneumonia detection (iii) Talk to patients and check
(v) Appointment their health status
Figure 4: Proposed system modules.
according to their time, can cancel appointments anytime, Figure 5 shows a few images from the real dataset. The whole
and send a reminder to patients anytime. dataset can be accessed through https://drive.google.com/
The following are included in this module: file/d/1nM5PVOjN_6ZbdjatfvPmPbi9K7RJdfIu/view?usp�
sharing.
(i) All appointments that the doctors have scheduled
Figure 6 shows the steps to detect the tumour from a dataset
are visible to them
image and the processing steps which have been applied to it.
(ii) Outpatients and patients who have been assigned to
their care are listed as follows
(iii) Possibility of diagnosing and assigning tests 4.3.2. Pneumonia Detection. Pneumonia is a lung infection
that causes inflammation in the air sacs of one or both lungs.
When the air sacs fill with fluid or pus, it can cause phlegm or
4.3. Radiologists. In regard to diagnosing and monitoring pus, cough, fever, chills, and difficulty breathing (purulent
diseases, radiologists benefit from the ML-based Smart material). Bacteria, viruses, and fungi are among the or-
Healthcare Model. In image identification tasks, machine ganisms that can cause pneumonia. Symptoms of pneu-
learning algorithms have made remarkable progress. ML mostly monia range from minor to life-threatening. For pneumonia
assesses radiological graphs and recognizes complex patterns in detection, authors used the Convolutional Neural Network.
the form of images automatically. Through transfer learning, the It requires a dataset of images for detection; this dataset
CNN has been utilized to detect brain tumour and pneumonia consists of 10000 images that detect bacterial and no
disease in this model. Glioma, Meningioma, Pituitary, and No pneumonia. Figure 7 shows images from the real dataset. The
Tumour are among the four types of tumours that can be whole dataset can be accessed through https://drive.google.
diagnosed with this 4000-image collection. As we all know, com/drive/folders/1yERz-JSZ2BEGZatS7vjt5ZsqfpNCFdyg?
brain tumours are one of the most prevalent and dangerous usp�sharing.
types of tumours that require prompt diagnosis and treatment.
Brain tumour screening is required to detect the disease. ML can
automatically detect nodules and classify them as cancerous or 5. Key Technologies for Smart
benign. In this case, machine learning-based algorithms not Healthcare System
only slow the progression of chronic diseases but also aid ra-
diologists in identifying patients with critical problems and 5.1. Machine Learning. It can be used to detect numerous
treating them first. disorders such as tumours and pneumonia, which require a
vast amount of data. The system can diagnose diseases ac-
curately thanks to a solution that is data-driven and mod-
4.3.1. Brain Tumour Detection. A brain tumour is a col- elled with supervised or unsupervised learning. Regression
lection of abnormal cells in your brain that has grown into a analysis, decision trees, Bayesian networks, correlation
mass. Brain tumours come in a wide variety of forms. Some analysis, clustering analysis, dimension reduction, and ar-
brain tumours are Glioma, Meningioma, and Pituitary. For tificial neural networks are all common machine learning
brain tumour detection, authors used the Convolutional methods [18]. Figure 8 shows different applications of
Neural Network. It requires a dataset of images; this dataset machine learning in the field of healthcare. Machine learning
consists of 14000 images that detect four types of tumours. is capable of identifying trends in previously unknown6 Computational Intelligence and Neuroscience
image (1) image (2) image (3) image (4) image (5)
image (6) image (7) image (8) image (9) image (10)
image (11) image (12) image (13) image (14) image (15)
image (16) image (17) image (18) image (19) image (20)
gg (1) gg (2) gg (3) gg (4) gg (5)
gg (6) gg (7) gg (8) gg (9) gg (10)
gg (11) gg (12) gg (13) gg (14) gg (15)
Figure 5: Dataset of test and train for brain tumour detection.Computational Intelligence and Neuroscience 7
Evaluating Model on the basis of Test Data
Train Model to recognize the tumor
Apply Model to new data
Disease Name : - Giloma Tumor
Probability : - 100%
Figure 6: Architecture of brain tumour detection.
medical data, providing therapeutic and diagnosis plans, and One of the pretrained models is Image Net. It will have an
making suggestions to unique patient-specific healthcare impact on the outcome [21]. To overcome this issue, clas-
experts [19]. Cloud storage infrastructures therefore should sification stages are performed using a pretrained model
be built to assist machine learning on large data volumes. based on a brain dataset.
5.2. Deep Learning. Artificial neural networks are algorithms 5.4. Image Processing. The term “digital image processing”
inspired by the structure and function of the brain, and deep refers to the use of a digital computer to process digital
learning is a branch of machine learning that deals with images. We may also call it the application of computer
them. It should be built on a foundation of large amounts of techniques to improve an image or extract relevant infor-
data. The accuracy of the learning algorithm is directly mation. The technical examination of pictures using com-
influenced by the data level [19]. Figure 9 shows the in- plex technologies is known as image processing [22]. The
fluence of different algorithms in the field of healthcare. image is taken as the input, and valuable data are returned as
Deep learning has a positive impact on visualization, natural the output [23]. Image processing is the process of en-
language processing, and multimedia data processing at the hancing or extracting information from pictures. The need
same time. Authors employed the CNN Deep Learning for a digital computer to perform an algorithm on digital
method for illness identification [19]. pictures is known as digital image processing [23].
5.3. Convolutional Neural Network. Convolutional Neural 5.5. Transfer Learning. It is a learning machine strategy that
Networks are made up of neurons with learnable weights employs a model developed to perform every task as the
and biases, just as regular neural networks. Each neuron basis for every other model of work. In deep learning, there
takes some input, does a dot product, and then executes a are many examples of transfer learning [10]. This process
nonlinearity if desired. A ConvNet requires far less pre- involves utilizing information gained in one activity to a
processing than other classification approaches [19]. A problem in a related target task. While most machine
Convolutional Neural Network is made up of multiple learning algorithms are designed to solve a particular
layers. Artificial neurons are computational models that problem, the development of algorithms that allow for
calculate the weighted sum of several inputs and output an transfer learning is a major topic within the machine
activity value, resembling biological neurons in appearance. learning field [10].
Early diagnosis improves the chances of survival and a MobileNetV2 architecture: MobileNetV2 expands on
healthy lifestyle for some disorders, such as cancer, while the concepts of MobileNetV1, including depth-wise sepa-
also reducing treatment expenses. The training and testing rable convolution as a profitable building component. Deep
phases of the CNN-based brain tumour categorization are network access via personal mobile devices increases the
separated. The number of pictures is split into subcategories convenience of the user by providing access every time and
by labelling them with terminologies like tumour and from any location and offers security, isolation, and energy
nontumour brain imaging and so on [19, 20]. Figure 10 savings [10]. The requirement for more efficient neural
shows the different steps for the dataset to detect different networks grows as new applications develop, which allow
diseases. To create a prediction model, preprocessing, feature users to interact with the actual environment in real-time.
extraction, and classification using the loss function are MobileNetV2 is a significant improvement over Mobile-
conducted in the training phase. To begin, identify the NetV1 in the category of object identification and semantic
training image. Image Net provided the brain image dataset. segmentation for visual recognition in mobile.8 Computational Intelligence and Neuroscience
person1_virus_6 person1_virus_7 person1_virus_8 person1_virus_9 person1_virus_11 person1_virus_12 person1_virus_13 person3_virus_15 person3_virus_16 person3_virus_17 person8_virus_27
person8_virus_28 person10_virus_35 person11_virus_38 person14_virus_44 person15_virus_46 person16_virus_47 person17_virus_48 person18_virus_49 person19_virus_50 person20_virus_51 person21_virus_52
person21_virus_53 person22_virus_54 person22_virus_55 person23_virus_56 person24_virus_58 person25_virus_59 person26_virus_60 person28_virus_62 person28_virus_63 person29_virus_64 person30_virus_69
person31_virus_70 person32_virus_71 person33_virus_72 person34_virus_76 person35_virus_80 person36_virus_81 person37_virus_82 person38_virus_83 person38_virus_84 person39_virus_85 person40_virus_87
person41_virus_88 person42_virus_89 person43_virus_92 person44_virus_93 person44_virus_94 person45_virus_95 person46_virus_96 person47_virus_99 person48_virus_100 person49_virus_101 person50_virus_102
person1_bacteria_1 person1_bacteria_2 person2_bacteria_3 person2_bacteria_4 person3_bacteria_10 person3_bacteria_11 person3_bacteria_12 person3_bacteria_13 person4_bacteria_14 person5_bacteria_15 person5_bacteria_16
person5_bacteria_17 person5_bacteria_19 person6_bacteria_22 person7_bacteria_24 person7_bacteria_25 person7_bacteria_28 person7_bacteria_29 person8_bacteria_37 person9_bacteria_38 person9_bacteria_39 person9_bacteria_40
person9_bacteria person10_bacteria person11_bacteria person12_bacteria person12_bacteria person12_bacteria person13_bacteria person13_bacteria person14_bacteria person15_bacteria person16_bacteria
_41 _43 _45 _46 _47 _48 _49 _50 _51 _52 _53
person16_bacteria person16_bacteria person17_bacteria person18_bacteria person19_bacteria person19_bacteria person19_bacteria person19_bacteria person19_bacteria person19_bacteria person20_bacteria
_54 _55 _56 _57 _58 _59 _60 _61 _62 _63 _64
Figure 7: Dataset of tests and trains for pneumonia detection.Computational Intelligence and Neuroscience 9
Disease diagnostic Healthcare related
and detection discoveries
Drug discovering Machine Learning Health
and manufacturing Application in monitoring
Healthcare
Epidemic outbreak Disease risk
prediction prediction
Figure 8: Machine learning in healthcare.
6. Comprasion with Other Systems
In recent years, many advanced technologies have been used
10.3% to improve healthcare systems. For example, IoT-based
wearable watches to count pulse rate and heartbeat, many AI
models have been introduced. However, in machine
7.5%
learning-based techniques, many algorithms advance the
39.3%
healthcare system like disease detection through SVM and
CNN. Thus, there are many algorithms in machine learning
which are stated below.
35.5%
6.1. K-Nearest Neighbor (KNN). The KNN algorithm is a
high-efficiency classification technique. We used different
feature selection techniques to improve efficacy and clas-
sification accuracy. The use of distance metric learning for
the input space of data from a specified collection of similar/
dissimilar points that preserves the distance relation among
the training data, and the application of the KNN algorithm
Support Vector Machine Random Forest
in the new data patterns, is another technique to increase
Neural Network Naive Bayes
accuracy. The K-Nearest Neighbor Technique is a classifier
Logistic Regression Nearest Neighbour that calculates the Euclidean distance across dataset input
Discriminant Analysis Other photos. On this dataset, it is proven that KNN and CNN
perform similarly to their respective algorithms, with the
Figure 9: Different algorithms contributions in healthcare field.
CNN producing higher accuracy than KNN and so being
picked as the best technique [9]. Lately, a picture is con-
verted into a close-to-the--bottom but exclusionary feature
vector to enable the training of discriminatory functions
Start directly from the original photographic data [9]. A three-
dimensional loss function has been presented. However, on
the other hand, it is separated from the KNN classification
stage by the CNN training technique. It would be better to
Data Acquisition consolidate feature extraction and classification into a single
phase in line with the existing CNN classifications.
Data pre-processing 6.2. Support Vector Machine (SVM). The coevolutionary
neural network is one of the most successful models for
image categorization difficulties (CNN). To address such a
recognition problem, researchers have traditionally had to
CNN Classification pretrain their human activity identification algorithms by
extracting certain features from different types of descrip-
tors, such as extended SURF [2] and STIPs [24], before they
are incorporated into a particular prediction model such as
End HMM, SVM, etc. Because of their poor performance and
memory usage needs, previous approaches are not very
robust [7]. The Support Vector Machine (SVM) offers a high
Figure 10: Algorithm steps for CNN. classification accuracy as well as good fault tolerance and10 Computational Intelligence and Neuroscience
generalisation capabilities. The Rough Set Theory (RST) 90
technique provides the benefit of dealing with vast amounts
of data and removing unnecessary material. Improved
Support Vector Machine is what happens when an SVM 80
classifier and an RST classifier are combined (ISVM). This is
an excellent tool for classifying medical images. The ex-
perimental findings reveal that this ISVM classifier has an
70
accuracy of 96.56 percent, which is 3.42 percent higher than
SVM’s 92.94 percent, and the error recognition rates are near
to 100 percent on average.
60
Regression
KNN
SVM
NB
Random
Forest
Logistic
6.3. Naive Bayes. The probability of occurrence is a function of
related events, according to the Bayes theorem, one of the oldest
machine learning algorithms (47). P(y | x) � [P(y), P(x | y)]/P(x) ACCU...
is the Bayes theorem formula: the probability (P) of y given x SPECI...
equals the probability of y times the probability of x given y, Figure 11: Graph showing performances of different algorithms.
divided by the probability of x. In machine learning, where
numerous input features are provided, the probabilities of each
feature must be chained together to compute the final
probability of a class given the array of input features. For
a given data and class, where a class signifies a random
variable, the conditional probability that an event belongs
to a class can be determined using the preceding equation.
The CNN classifier model is compared to the Naive Bayes
Classifier (NBC) model to establish whether the classifier
can provide greater accuracy in the healthcare sector [25].
The research approaches to be employed in this study
include data collection, preprocessing, model design and
training, model testing, and visualization. The CNN
classification model has an accuracy of 0.88 or 88 percent
in the testing phase of the data test, while the NBC
classification model, according to the study results, has an Disease Name : - Giloma Tumor
accuracy of 0.78 or 78 percent [20]. Based on these Probability : - 100%
findings, the deep learning classifier model provides
greater precision than the Naive Bayes classification Figure 12: Detecting brain tumour.
model. Figure 11 shows the accuracy and specificity of
different algorithms.
In Table 1, different techniques are given here and these
techniques are compared to each other in accuracy and
specificity. In these different techniques, the accuracy of
SVM is more than other techniques.
7. Result Analysis
The data used in this study were obtained from Kaggle.
Those datasets were created for the medical imaging research
community’s research purposes. This repository contains
4700 image slices that were used during the research’s Disease Name : - Bactorial Pneumonia
training and testing stages. During the training stage, 50 Probability : - 100%
patients are used, and during the testing stage, 20 patients
are used. Figure 13: Detecting bacterial tumour.
The experiment’s outcomes are depicted in Figures 12
and 13. The CNN-based classifier is faster, taking only
30 seconds for both training and testing sessions, compared Figure 14 shows the result of two combined techniques;
to 58 seconds for SVM. Despite having an additional step these are SVM and CNN. In this way, the authors created a
feature extraction, this CNN result still has a faster com- hybrid approach to find the effectiveness of algorithms on
putation time. disease detection. The combination of these two methodsComputational Intelligence and Neuroscience 11
0.9 Disclosure
0.8
SVM (PNEUMONIA (1))
0.7 This article is distributed under the terms of the Creative
0.6 Commons Attribution.
0.5
0.4 Conflicts of Interest
0.3
0.2 The authors declare that they have no conflicts of interest.
0.1
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Authors’ Contributions
Stack (NORMAL (1))
All authors contributed equally and significantly in writing this
Figure 14: Stack (SVM + CNN). article. All authors read and approved the final manuscript.
shows the high level of prediction compared to a single References
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