International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
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Distance-Based Optimization Model for Heart Failure Risks Prediction in India Dr.G.Arutjothi1, Dr.S.Tamilsenthil2 1,2 Assistant Professor, Dept. of Computer Applications,
Sona College of Arts and Science, Salem-5,Tamil Nadu, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Today, deep learning AI (artificial intelligence) is widely used in healthcare to diagnose diseases. Rheumatic heart disease (RHD) and coronary artery disease (CAD) are the main causes of heart failure (HF), which is still a serious problem in India. Given that there are currently about 30 million cases of coronary heart disease (CHD) in the nation, it is evident that the effects of heart failure range greatly depending on the age group, with younger people experiencing greater mortality rates and less favourable treatment outcomes. This underscores the urgent need to establish comprehensive guidelines for heart failure treatment tailored to the Indian context. Early detection is crucial for reducing heart failure rates, and leveraging historical data through deep learning (DL) technology can play a pivotal role in this regard. Our study aims to enhance early heart failure detection and recommend appropriate treatments using deep learning techniques, which have shown the highest accuracy for our dataset. We also compare this model's performance with other machine learning models to validate its efficacy. Even while our results show that deep learning models can be used to diagnose and forecast heart failure, more thorough and sophisticated study is still required to further incorporate technology into healthcare. By advancing these methods, we can significantly improve the early detection and management of heart failure in India.
speed, as evident in image identification, speech identification, and other domains (Vargas et al., 2017). Deep learning has made significant inroads into scientific domains, often leading to entirely new solutions and hypotheses, such as in protein folding, semiconductor chip design, and even mathematics (Liu et al., 2021). However, deep learning systems also raise concerns related to workforce issues, privacy, transparency, and ethical use (Vargas et al., 2017).
Key Words: Deep Learning(DL), Artificial Intelligence(AI), Heart Failure(HF), Accuracy, Precision, Healthcare Technology.
In this research, we propose an ensemble model for predicting heart failure and warning of heart failure risks. This model's ability to analyze complex datasets promises significant advancements in early diagnosis and treatment.
1.2 HEART FAILURE Heart failure is a common type of cardiovascular disease that contributes significantly to global healthcare costs. It is an acquired clinical condition characterized by a decrease in cardiac function due to diverse causes, including CAD, hypertension, and cardiomyopathy (CM). Early diagnosis and follow-up are crucial for improving prognosis and reducing healthcare expenses (V. C A & Baby Shalini, 2023). Machine learning and its subfield, deep learning, have emerged as recent trends in the diagnosis and prognosis of medical conditions. Deep learning models can analyze large amounts of patient data, including clinical history, ECG signals, echocardiogram images, and physiological measurements, to identify important features related to heart failure development (Zhou et al., 2023).
1.INTRODUCTION
2. LITERATURE SURVEY
1.1 DEEP LEARNING
Heart disease continues to be the world's top cause of mortality, making it a serious public health concern (Robert Detrano et al., 1989). Deep learning and machine learning developments have created new opportunities for the precise prediction and early identification of cardiac disease, which may have a significant effect on patient outcomes. The use of several machine learning and deep learning algorithms for the prediction of heart disease has been the subject of numerous studies. In order to enhance the diagnosis of patients with heart disease, a hybrid generic framework was put out that integrated several machine learning approaches (Al-Alshaikh et al., 2024).
Artificial neural networks with multiple hidden layers are known as deep learning, which can be defined as a subgroup of machine learning. Applications like picture and audio recognition, natural language processing, and predictive analytics benefit greatly from the deep neural networks' ability to understand intricate patterns and high-level features present in high-dimensional data (Vargas et al., 2017). Recent developments in machine learning, data accessibility, and the appearance of alluring platforms and accelerators are some of the reasons for the growing interest in deep learning (Liu et al., 2021). These innovations have led to progress in handling various issues with high reliability and
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