International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
HEART DISEASE PREDICTION USING DEEP LEARNING TECHNIQUES 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 - Healthcare stands as a cornerstone of human
well-being, with cardiovascular disease posing a significant threat to the general populace. In the current era, characterized by prevalent sedentary lifestyles, a substantial portion of the population grapples with this issue. Existing methodologies often fall short in accurately predicting viralinduced diseases, marking them as perilous infections not only in India but across the globe. Statistics reveal that a staggering 28.1% of fatalities stem from heart disease, making it a leading cause of death, claiming over 17.6 million lives in 2021 post the Covid-19 pandemic. The delayed identification of heart ailments drastically diminishes patients' chances of survival. Consequently, the necessity for a precise and reliable diagnostic system for timely detection and treatment of such conditions becomes paramount. The innovative framework proposed leverages a sophisticated deep learning algorithm known as Convolutional Neural Networks (CNN) fused with advanced optimization techniques to enhance the accuracy and prognosis of heart disease from chest X-ray imagery. The primary objective of our model is to cultivate a precise and effective diagnostic mechanism boasting an 85.2% accuracy rate, thereby aiding healthcare practitioners in the prompt and early management of cardiovascular disorders. Key Words: Heart Disease Prediction, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Personalized Healthcare, Model Interpretability, Data Quality
1.BACKGROUND Heart disease remains one of the leading causes of mortality worldwide, contributing significantly to the global burden of disease. Early detection of heart-related conditions is crucial, as it can substantially improve patient outcomes by enabling timely interventions, lifestyle changes, and medical treatments. Identifying the risk factors or symptoms associated with heart disease at an early stage can prevent the progression to more severe conditions, such as heart attacks, strokes, or heart failure. The importance of early detection lies in its potential to reduce the strain on healthcare systems, lower mortality rates, and improve the quality of life for individuals. Early diagnosis allows healthcare professionals to implement preventative measures, manage the disease more effectively, and reduce the likelihood of complications. In many cases, heart disease is asymptomatic in its early stages, which © 2024, IRJET
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Impact Factor value: 8.315
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makes accurate and timely prediction methods even more critical. Traditional diagnostic techniques often rely on clinical examinations, patient history, or basic biomarkers, which can sometimes fail to detect underlying cardiovascular issues. This has led to the growing interest in leveraging advanced technologies, such as deep learning, to enhance the predictive accuracy and reliability of heart disease detection. Through the analysis of vast amounts of data, these techniques offer the potential to identify patterns and risk factors that might be missed by conventional methods, thereby playing a vital role in the early identification and prevention of heart disease.
2.DEEP LEARNING IS SUITABLE FOR HEART DISEASE PREDICTION Deep learning has emerged as a powerful tool in medical research and diagnosis due to its ability to process vast amounts of data and automatically extract meaningful patterns. When it comes to heart disease prediction, deep learning offers several advantages that make it particularly well-suited for this task. Firstly, heart disease prediction involves complex interactions between numerous risk factors such as age, blood pressure, cholesterol levels, and lifestyle habits. Deep learning models, especially neural networks, excel at capturing these intricate, non-linear relationships that traditional statistical methods may overlook. By leveraging these models, deep learning can provide more accurate predictions by learning from large datasets that contain diverse patient data. Secondly, deep learning models have the ability to learn from both structured and unstructured data. In the context of heart disease prediction, this is highly beneficial as data sources may include structured medical records, laboratory results, and patient demographics, alongside unstructured data like medical images and clinical notes. Deep learning's ability to integrate these different data types allows for a more comprehensive analysis of a patient’s overall health, leading to more precise risk assessments. Another significant advantage is that deep learning algorithms can continuously improve their performance as more data becomes available. This adaptability is particularly important in healthcare, where new patient data can be used to refine models over time, leading to more reliable predictions. Furthermore, deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promising results in handling temporal and spatial data, which can be crucial in detecting subtle changes ISO 9001:2008 Certified Journal
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