International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024
p-ISSN: 2395-0072
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HEART DISEASE PREDICTION USING DEEP LEARNING TECHNIQUES : A REVIEW Priya Dubey1, Dipti Ranjan Tiwari2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Heart disease continues to stand as a prominent
contributor to global mortality rates, underscoring the imperative for the creation of precise and effective predictive frameworks. In recent times, deep learning methodologies have garnered considerable interest due to their capacity to scrutinize intricate and multi-dimensional datasets. This scholarly paper delves into the utilization of deep learning approaches in forecasting heart diseases, scrutinizing a spectrum of models and algorithms deployed in this sphere. The manuscript furnishes a comprehensive survey of diverse deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and amalgamated models, accentuating their efficacy in processing medical data and refining prognostic precision. Furthermore, the review deliberates on the hurdles and constraints encountered by deep learning models in the realm of heart disease prediction, encompassing challenges associated with data integrity, model interpretability, and generalizability. Ultimately, the paper delineates forthcoming avenues for research, underscoring the promise of amalgamating deep learning with other nascent technologies to enrich predictive capacities and contribute to tailored healthcare solutions. Key Words: Heart Disease Prediction, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Hybrid Models, Medical Data Analysis, Predictive Models, Personalized Healthcare, Model Interpretability, Data Quality
1.HISTORY Heart disease, a prominent cause of mortality on a global scale, boasts a rich and intricate historical background. Ancient civilizations, such as the Egyptians and Greeks, meticulously chronicled symptoms now attributed to heart disease, albeit without a comprehensive grasp of its underlying causes. It wasn't until the 17th century that William Harvey elucidated the intricacies of the circulatory system, thereby laying the foundation for contemporary cardiology. Through the passage of time, breakthroughs in medical science unveiled the complex mechanisms of the heart and shed light on the profound influence of variables like diet, lifestyle, and genetic predispositions on cardiovascular well-being. Presently, heart disease, encompassing maladies like coronary artery disease, heart failure, and stroke, stands as a formidable global health
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crisis, claiming an estimated 17.9 million lives annually as reported by the World Health Organization. This burden is notably weighty in regions characterized by limited healthcare accessibility and preventive interventions, particularly in low- and middle-income nations. The worldwide repercussions of heart disease underscore the imperative of ongoing research endeavors, early detection initiatives, and innovative therapeutic approaches geared towards diminishing mortality rates and enhancing overall quality of life.
1.1.Importance of early detection and prediction in reducing mortality rates. Early detection and anticipation of cardiovascular disease are paramount in decreasing mortality rates, as they facilitate timely intervention before the condition escalates to a critical phase. By pinpointing predisposing factors and symptoms at an initial stage, healthcare providers can implement preemptive measures, lifestyle modifications, and medical interventions that can substantially delay or even avert the onset of severe cardiac conditions. Timely diagnosis also enables the creation of tailored treatment strategies, which can be more efficacious in managing the ailment and mitigating complications. Furthermore, prognostic models, especially those driven by cutting-edge technologies like deep learning, can evaluate individual risk with remarkable precision, enabling proactive care customized to each patient. This proactive stance not only enhances survival rates but also enriches the quality of life for patients by diminishing the probability of life-threatening incidents such as myocardial infarctions or cerebrovascular accidents. In essence, early detection and anticipation are pivotal tactics in the worldwide battle against cardiovascular disease, offering the potential to preserve millions of lives annually.
2.INTRODUCTION Heart disease, a prevalent health issue, encompasses a wide range of conditions that impact the structure and function of the heart. Among these, coronary artery disease stands out as the most common form, characterized by the narrowing or blockage of arteries supplying blood to the heart. This obstruction, often caused by the buildup of plaque, restricts blood flow and heightens the risk of heart attacks. Additionally, heart failure, where the heart struggles to
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