A COMPREHENSIVE SURVEY ON HEART DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING APPROACHES - Research survey

Aug 5, 2026

Heart disease remains a leading global cause of mortality, making early detection and accurate cardiovascular diagnosis essential for reducing medical burden and improving outcomes. This survey reviews state-of-the-art heart-disease prediction using machine learning and deep learning, covering models such as logistic regression, decision trees, support vector machines, random forests, neural networks, CNN, and LSTM. It examines datasets, feature selection, preprocessing methods, evaluation metrics for accuracy and reliability, and compares model benefits and limitations. It also addresses data imbalance, interpretability, privacy, deployment constraints, and future directions including explainable AI, federated learning, and multi-modal health data integration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Global burden of cardiovascular disease and need for early prediction

Conventional diagnostics vs. intelligent automated approaches

Machine learning and deep learning methods for HD prediction

Data sources, feature preprocessing, and publicly available datasets

Hybrid models and performance considerations

Challenges: imbalance, overfitting, interpretability, privacy, deployment

Keywords

20(3): S. I (3), 268-276, 2025 [www.thebioscan.com](www.thebioscan.com) A COMPREHENSIVE SURVEY ON HEART DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING APPROACHES 1*S. Selvapriya, 2Dr. M. Saranya 1*Research Scholar, P.K.R. ARTS COLLEGE FOR WOMEN, Gobichettipalayam, Erode (DT), TamiNadu, India. 2Research Supervisor, Associate Professor, P.K.R. ARTS COLLEGE FOR WOMEN, Gobichettipalayam, Erode (DT), TamiNadu, India. E-mail: Id:1*[selvapriya.2020@gmail.com](selvapriya.2020@gmail.com), [2](2drmsaranyamcapersona@gmail.com)[drmsaranyamcapersona@gmail.com](2drmsaranyamcapersona@gmail.com) DOI: 10.63001/tbs.2025.v20.i03.S.I(3).pp268-276 KEYWORDS Machine Learning (ML), Deep Learning (DL), Long Short-term Memory (LSTM), Heart Diseases, Convolutional Neural Network (CNN), logistic regression, decision trees, support vector machines, random forests, neural networks. Received on: 08-06-2025 Accepted on: 02-07-2025 Published on: 06-08-2025 ABSTRACT Globally, the death rate is increased by one of the major conditions named heart disease (HD) . This HD greatly impacts the global healthcare systems. For the purpose of enhancing outcomes of the patient and reducing medical challenges, the early detection (ED) and diagnosis of cardiovascular disease (CVD) is crucial. Then, the implementation of the artificial intelligence (AI), namely machine learning (ML) and deep learning (DL) techniques have revolutionized the predictive modelling of HD. A comprehensive insights regarding the recent developments in HD prediction with the application of the machine learning (ML) and deep learning (DL) algorithms, including logistic regression (LR), decision trees (DT), support vector machines (SVM), random forests (RF), neural networks (NN), convolutional NN (CNN), and long short-term memory (LSTM) models was offered in this study. Here, the commonly utilized datasets, feature selection (FS) strategies, data pre-processing approaches are all examined in this study. Then the study also analyses the assessment metrics that will helps in determining the accuracy (ACC) and dependability of the predictive models (PM) . The benefits, drawbacks, and efficacy of every model is identified by the comparison of models. This survey also facilitates in resolving issues like data imbalance, model interpretability, privacy issues, and practical deployment limitations. Recommendations regarding future directions, like explainable AI, federated learning (FL), and the integration of multi-modal (MM) health data was also offered in this study, and it may help the experts in creating more clinically valuable and dependable prognostic tools. This comprehensive survey contributes the scholars and professionals in creating intelligent systems for HD diagnosis and risk assessment. So, this comprehensive survey is beneficial. INTRODUCTION According to the World Health Organisation (WHO), CVD, especially HD, is still the leading cause of mortality worldwide, taking the lives of around 17.9 million people year [1] . Heart failure (HF), arrhythmias, coronary artery disease, and other illnesses affecting the heart and blood vessels (BV) are included in this category. Even though conventional diagnostic techniques like electrocardiograms (ECG) [2], echocardiography [3], and angiography [4] have proven successful, they are frequently costly, time-consuming, and interpreters need to be trained. Then, after the disease progression in patient, the diagnosis will be given. This carelessness may reduce the possibilities in giving an effective treatment. So, in this case, the development of intelligent and automated solutions is demanded, and it is facilitated by the ED and precise prediction of HD. Hence, ED and precise prediction of the HD is crucial for giving a patient immediate and effective care [5] . The diagnosis and prediction of the cardiac-associated conditions are revolutionized by the application of the computational intelligence (CI),

FAQs

Why is early detection of heart disease critical?

Early detection and precise prediction of heart disease support timely and effective patient care, reducing medical challenges and improving outcomes in cardiovascular conditions.

Which machine learning and deep learning models are reviewed for heart disease prediction?

The survey covers logistic regression, decision trees, support vector machines, random forests, neural networks, convolutional neural networks (CNN), and long short-term memory (LSTM) models.

What evaluation and dataset-related aspects does the survey emphasize?

It examines commonly used datasets, feature selection and data preprocessing approaches, and analyzes assessment metrics used to determine predictive accuracy and dependability, including comparisons across models.

Aug 5, 2026

Upload by Clementine