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Computer-aided Bio-medical Tools Disease Prediction using Machine Learning 73
https://t.me/med1917
On similar lines, Mazurowski et al. [72] developed user models using three
different approaches: k-nearest neighbors, artificial neural network, and multiple
regression analysis. Zhang et al. [73] designed a computer model to identify areas
where false positive mistakes are more likely to occur. In this case, the CADbased machine-learning technique is a random forest trained using residential
observations to predict the likelihood of future mistakes. Computer vision
techniques were utilized for both potential false positive segmentation and lesion
characteristic extraction [74].
The findings indicate that diseases such as DR, AMD, and glaucoma can be
detected with good sensitivity, precision, and AUC using CAD from both RFP
and OCT scans in ophthalmology. The data also show that CAD has high
sensitivity, specificity, and AUC for detecting chest pathology on CT scans and
CXR in radiologic technology. While CAD on CT exhibited higher sensitivity and
AUC, CXR showed better specificity, PPV, and F1 score. Additionally, CAD on
CT demonstrated significantly better sensitivity than CXR in detecting cancer or
pulmonary masses. Although CAD systems are not 100%accurate, their hit rate
suggests that sensitivity can now reach approximately 98%. However, the
accuracy of CAD depends on various factors, such as the characteristics of the
images used to train the model and retrospective design. Factors like image
quality, mammography test conditions, radiologists' markings, lesion type, and
tumor size and location all significantly impact CAD accuracy. On the other hand,
the diagnostic accuracy of MRI was poorer, possibly due to limited datasets and
the use of 2D images. The use of larger datasets and multi-parametric MRI could
potentially improve diagnostic accuracy. To create an individually tailored
computer-aided detection system in mammography, single and hybrid ANNs tools
have been employed in biomedical image assessment.
CONCLUSION
Developers, medical practitioners, and researchers face challenges in medical
image-based disease diagnosis. There is a strong need for high-efficiency CAD
tools for reliable and early diagnosis of tissue or organ anomalies. CAD software
tools in radiology help radiologists read studies faster and more efficiently,
reducing false negatives and enabling quick analysis of follow-up scans for
incorrect diagnoses.
Several factors are driving the global CAD industry's growth, including increasing
cancer incidence, technological advancements, growing awareness of regular
health check-ups, innovations in CAD technology, and its use in infections
beyond cancer. The implementation of CAD in routine care practice may lead to a
better understanding of its advantages and risks in geriatric medicine. The
74 Disease Prediction using Machine Learning Irudaya Rani et al.
https://t.me/med1917
potential benefits of CAD-based clinical decision support enhance its utilization
in healthcare.
To improve CAD usage, consistent guidance on research design and reporting is
necessary, which may explain its long-term clinical value. However, a shift
toward hybrid image analyzing tools should be carefully considered to avoid
sidelining patients in the medical decision-making process. It is crucial to include
explainability in clinical decision support systems to uphold medical ethical
principles and protect individual and public health.
Efforts are needed to raise awareness of the limitations and challenges of CAD in
biomedical imaging analysis among developers, healthcare practitioners, and
researchers. Interdisciplinary collaboration can play a key role in addressing these
issues in the advanced biomedical research field.
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CHAPTER 5
Prognosis of Dementia using Machine Learning
Anu Saini1, Sunita Kumari
1
GB Pant DSEU, Okhla-1 Campus, Delhi, India
2
Amity University, Dubai Campus, Dubai, United Arab Emirates
Abstract: The brain is one of the most sensitive parts of the human body which
transmits millions of signals every moment. Dementia is the most emerging brain
health issue which involves memory loss, difficulty in problem-solving, handling
complex tasks, etc. Dementia is a syndrome that causes a loss of mental ability. It
affects memory, thinking, shape, comprehension, counting, reading ability, language,
and judgment. Dementia affects millions of people and can be the leading cause of
death. It is now the seventh leading cause of death worldwide, as well as one of the
major causes of impairment and reliance on elderly people. There is no treatment for
dementia at present. The importance of early detection and diagnosis in improving
early and effective management is crucial. Predicting dementia in advance can lead us
to a better life. To predict dementia, various Machine Learning models have been used.
In this paper, Dementia is predicted on the basis of MRI Images, for this, three
different datasets of MRI Images have been collected. Furthermore, for better
prediction, various Machine learning models are used to predict dementia and validate
the performance with statistical analysis like K-Nearest Neighbours, XG Boost,
Support Vector Machine, Random Forest Algorithm (RFA), and Convolutional Neural
Network (CNN). Out of all algorithms, Random Forest Algorithm and Convolutional
Neural Network gave the best result with the accuracy of 93.2 and 99.9 respectively.
1,*
, Ritik 1, Rajni 1 and Sushma Hans
2
Keywords: Alzheimer's disease, CNN (Convolutional Neural Network),
Dementia, Random forest algorithm.
INTRODUCTION
Dementia is a neuropsychological disorder that causes memory loss leading to
paralysis and reliance on others for survival. It is most frequent in those over 60
years, with an estimated 900 million people in their 60s suffering from it. It
affects more than 46 million individuals globally, more than the whole populace
of Spain. By 2050, this number is predicted to surge to 131.5 million [1]. Due to
their severe mental incapacity, Alzheimer's disease and other major dementias
*
Corresponding author Sunita Kumari: GB Pant DSEU, Okhla-1 Campus, Delhi, India;
E-mail: sunita2009@gmail.com
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
All rights reserved-© 2024 Bentham Science Publishers
Prognosis of Dementia Disease Prediction using Machine Learning 81
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affecting older people inflict a substantial financial and social burden on families
and communities, with Alzheimer's account for over 75% of cases.
Dementia affects approximately 50 million people worldwide, as per the World
Health Organization (WHO), with an estimated 10 million new cases expected
annually.
Dementia is clinically diagnosed based on a comprehensive health history
provided by patients and families, as well as neurological tests followed by a
cognitive assessment. Tests such as hematology, CT, and MRI should be
performed to diagnose the cause of dementia. Neuropsychological exams are
important for identifying inefficiencies in human “cognitive domains”. Although
there are a few clinical steps for the early detection of dementia, there are still
many challenges.
Once it is diagnosed, currently there is no viable medication for dementia that can
halt or stop its growth. Hence, it becomes critical to concentrate on the early
phases, prompt intervention, and disease prevention. Timely diagnosis can decide
the degree of dementia, and neuro-imaging analysis can aid in your diagnosis.
Further, it is better if we can predict whether a person can be affected by dementia
in his/her future years. The condition is predicted using a variety of machinelearning technologies. This research aids in determining how Deep Learning can
be used to predict dementia using MRI pictures.
We employed two methods to achieve the highest accuracy of our model,
Convolutional Neural Network (CNN) for image-based analysis and RFA-based
statistical analysis.
CNN is a robust image processing and artificial intelligence (AI) system that uses
in-depth learning to execute productive and descriptive functions, often
combining image and video recognition and complimentary algorithms as well as
natural language understanding (NLU). The neural network is a hardware and/or
software system that controls the pattern in the human brain once neurons are
activated. Traditional neural networks aren't well adapted to image processing,
thus they should be provided with fragmented images. CNN has its own
incredibly sophisticated “sensors” similar to those used in the old record, a site
dedicated to human and animal visual processing. To solve the difficulty of
standard neural networks processing images, the layers of neurons are structured
in such a way that they span the full observing field.
CNN employs a multilayer perceptron technology with low processing
requirements. CNN layers comprise several flexibility layers, integration layers,
82 Disease Prediction using Machine Learning Saini et al.
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completely integrated layers, and custom layers, as well as an input layer, an
output layer, and a hidden layer. The removal of image processing limitations and
greater efficiency results in a more efficient, simple-to-use training system that
limits image and natural language processing.
Random Forest is a machine-learning strategy for dealing with setbacks and
planning issues. It employs integrated learning, which is a multidisciplinary
method to solve complex issues. The random forest algorithm comprises a large
number of trees that can be pruned. Combining bags or bootstraps is used to train
a 'forest' formed by a random forest algorithm. Bagging is a set of metaalgorithms that help machine learning algorithms increase their accuracy. The
result is determined by the algorithm (random forest), which is based on decision
tree prediction. It makes predictions based on the rate of output of different trees.
The accuracy of the output improves as the number of trees grows.
The random forest overcomes the decision tree's algorithmic limitations. It
reduces data set overload and improves accuracy. Predictions are generated
without the requirement for various package configurations (like sci-kit-learn).
Random Forest Algorithm Characteristics:
• Provides an effective technique to handle missing data.
• More accurate results as compared to the decision tree algorithm.
• Can provide sound guesses without adjusting the upper parameter.
• Resolves decision tree overcrowding.
The rest of the paper is structured as follows:
The related work done by various researchers is found in section two. Section
three discusses the methodology proposed by the authors. Section four describes
the experimental work and result analysis. Finally, in section five, there is a
conclusion.
RELATED WORK
Literature shows that lots of researchers used different machine learning models
to predict dementia. Some of the works are discussed in detail below.
The authors of the paper [1] gathered data from 5,272 people who completed a
survey having 37 items. They focused on the three alternative ways of selecting
features in order to find the most significant ones. The diagnostic models are then
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