Cardiovascular diseases (CVD) are cited as a leading cause of CVD complications and death records similar pronouncements by the American Heart Association that these diseases are responsible for 179 million deaths worldwide annually (Prabhakaran et al., 2022). These figures should also highlight the need to enhance resources for biomedical research and the development of new approaches to managing cardiovascular diseases and their risks worldwide (Vaduganathan et al., 2022).
Annually hearing resources a small number of them Medical Complications of cardiovascular cardiovascular healthcare concern is enormous CVD causes deep concern within the global health community (Flores-Alonso et al., 2022). It is essential that the conquering of CVDs should be the most fundamental priority with respect to the quality of life and import mortality (Sarrafzadegan & Mohammmadifard, 2019). Despite the fact that auscultation of the heart is a simple, and highly reliable, intervention for detecting CVDs, it is also most times, a puzzle for even well-trained physicians to be able to do this within a short time (Yan et al., 2019). With the help of PCG classification deep learning different powerful strategies are proposed to increase the effectiveness of the automated cardiac screening systems, based on artificial intelligence, aiding working physicians in better decision-making (Sethi et al., 2022).
According to the WHO Organization which was constituted by the UN & other international organizations, heart disease is the number one killer worldwide. Cardiovascular diseases (CVDs) remain one of the great threats to health in every part of the globe, registering millions revered deaths every year (Shokouhmand et al., 2021). The Internet of Medical Things (IoMT) is a form of technology that connects medical devices for data acquisition and data processing in real-time which improves the processes in the health sector.
Research Motivation:
By analyzing enormous medical datasets, machine learning offers a low-risk, efficient, and perhaps more voracious strategy that uses machine vision to analyze data and discern risk factors for CVD. Medical specialists can apply machine learning techniques to the extent of predicting the risks of CVD on patients based on multiple clinical parameters such as the age of patients, illness history, and diagnostic assessments. In the end, this method enhances the health of patients and optimizes the use of health care’s economical aspects by enhancing diagnosis accuracy and promoting the use of targeted intervention approaches and preventive strategies.
Research Problem:
The machine learning approach to predict CVD carries many operational challenges with the principal problem being how to deal with the identification of high-risk individuals using dependable and generalizable models. There is a challenge of feature selection and feature engineering in that there is the necessity to include important variables without introducing bias, the challenge of the requirement for high dimension, good quality, and even representation of the risk factors, and the challenge of finding robust cognitive models that can deal with intricate risk factor interrelations. Indeed, developing clinical trust and thus how practitioners will understand and be able to carry out the predictions made entails the ability of the model to be interpretable and transparent. Another challenge is how to present such machine intelligence models into existing healthcare environments appropriately, in a way that is relevant to users, and that enhances rather than complicates clinical workflows. Tackling these challenges is necessary in order to devise robust and efficient tools for enhancing the early detection of CVD, guiding preventive actions, and eventually easing the care burden of illnesses causing cardiovascular diseases.
Research Scope:
Machinelearningisamorcellerisess… It is also the branch of engineering that employs computational techniques and statistical data to develop efficient instruments within an engineering discipline. It includes insurance and investment evaluation, marketing management, and financial forecasting. The primary ones are Logistics regression, Decision trees, and Support vector machines. Such techniques are often referred to as feature engineering, model selection and engineering, or other similar terms. These adjust the original data through transformation and normalization and distribute it as evenly as possible errors. When affecting clinical outcomes, the completed case of assumptions has problems emphasising comprehensive details and noise reduction. It is essential for machine learning, that the defined scope incorporates a performance assessment of the model with the use of measures such as accuracy estimate, precision, recall, and AUC-ROC. One more required step is the intelligibility of model results which prevents a problem of rather complex and blasted estimates resulting in the incapability of healthcare specialists to use the model easily.
Research Objectives:
The primary objectives of this thesis are as follows:
Creating a framework that will facilitate the incorporation of clinical records demographic information as well as other information in the data collection exercise to develop adequate models for training and testing.
To apply a number of machine learning methods in order to find and evaluate the most precise methods for predicting the risk of CVD.
To create novel advanced predictive feature selection and engineering techniques which facilitate the identification of the best predictors, assist in feature dimensionality reduction, and suppress data noise which will result in accurate as well as efficient predictive models
Research Question:
Try to answer the following question:
What are the optimal approaches that can be used to combine heterogeneous data sets such as clinical records and demographic data in developing a strong comprehensive dataset appropriate for model training and evaluation in the prediction of CVD risk?
What are the computational limits and practical challenges encountered in the application of different machine learning models in the field of health?
How would one achieve a reasonable trade-off between complexity and interpretation of the predictive models if such models were required to be very accurate in predicting CVD risk?
The Proposed Contribution Of The Dissertation:
This work is designed to construct the appropriate and necessary data set for the targeted model training and evaluation by proposing a new methodology that combines various data sources including demographic and clinical data. The aim of the dissertation that is formulated in the research is to find the optimal ways and techniques to predict the CVD risk using logistic regression, decision trees, support vector machines as well as different approaches to machine learning.
It will also comprise complex data reduction techniques which will include feature engineering and selection techniques that will serve to lessen the degree of data and noise while accentuating the key factors that will in turn enhance the precise models built. In order to enable practical use of these risk factors in practice, the study will involve the construction of such models that explain in detailed and specific actionable insights on CVD risk factors. The dissertation will assess the impact of these predictive tools on patients’ status, prevention strategies and early diagnosis through implementing them in real-life clinical practice. This will highlight to what extent machine learning can alter the predictions and management of cardiovascular diseases.
Dissertation Organization:
Chapter 1 states the problem, goals and motivation in predicting heart diseases with machine learning. Chapter 2 shares the findings that have been made with regard to cardiovascular disease prediction as well as the intersection between cardiovascular diseases and machine learning. This chapter examines the existing literature and provisions made by current research thereby creating the basis for this research and providing ways for creativity. Chapter 3 explains in detail the exhaustive framework that has been proposed for incorporating the various clinical and demographic data types from different physicians. It explains the chosen machine learning algorithms, the methods of feature selection and engineering that were invented, and the strategies and criteria that were defined for testing the developed models.
Most importantly these two chapters introduce the work and present findings as well as limitations and discussions. The final chapter in the book presents the conclusion and future direction.
Chapter Summary:
Where history has already gone there is the chapter on CVD. The factors determining the extent of the problem are chronic coronary and cardiovascular conditions. Most importantly this chapter states about the integer need to develop CVD prediction methods more precisely and in the shortest possible time. It indicates the likelihood that the prediction of CVD may be accurate, rapid and non-invasive through the integration of machine learning techniques. The chapter formulates the objectives of the proposed study such as enhanced feature selection in eds, applying various machine learning models, and development of a multisource integration framework.
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