Recent Advances in AI-Based Heart Disease Prediction: A Review of Machine Learning and Deep Learning Approaches

Rahul Kumar — 2026-06-18

The current modality of diagnosis for heart diseases lacks the sensitivity and specificity characteristics necessary for early and accurate diagnosis of this time-sensitive illness. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has demonstrated substantial potential in aiding cardiac health diagnosis and prediction. This review article provides a comprehensive review of ML and DL techniques used in predicting heart diseases. Additionally, it provides an overview of standard ML algorithms using K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Trees, Random Forests, and ensemble methods and advanced deep learning models such as Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) and hybrid architectures. The importance of data preprocessing, feature engineering, feature scaling, and data augmentation techniques that significantly influence its efficacy in a highly effective manner is brought to the fore in this review. Special attention is given to methods handling class imbalance, such as the Synthetic Minority Oversampling Technique (SMOTE), that improve the detection of minority classes and relevant medical datasets. In addition, optimisation strategies, activation functions, and regularisation techniques used in deep learning models are discussed to understand their role in improving the predictive accuracy and model generalisation. Algorithms and a hybrid algorithm for deep learning-related research show relatively better indications of performance in comparison to regression-based algorithms. To reach an even simpler precision, metrics must include accuracy, sensitivity, specificity, precision, F1-score, and AUC for model evaluation. Presented herein are also particularly creative reflections on further understanding of digital research and envisioning changes for future activities related to AI for the prediction of cardiac dysfunctions – because the fine-tuned discoveries will aid in general terms for evaluation and deployment as clinical feedback mechanisms.

How to cite this article:
Kumar R, Rai A K. Recent Advances in AI-Based Heart Disease Prediction: A Review of Machine Learning and Deep Learning Approaches. J Adv Res Appl Arti Intel Neural Netw 2026; 10(2): 1-9.

Predictive Healthcare Using Ayurveda and Machine Learning for Sustainable Bharat 2047

Ms. Sarpreet Kaur Gill — 2026-08-04

The rapid increase in chronic diseases, rising healthcare expenditure, unequal access to medical facilities, and growing environmental and demographic pressures have intensified the need for sustainable and preventive healthcare systems in India. Ayurveda, the traditional Indian system of medicine, offers a holistic framework centered on personalized healthcare, lifestyle regulation, preventive wellness, and mind–body balance. Simultaneously, advancements in Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), wearable technologies, cloud computing, and predictive analytics have transformed modern healthcare by enabling intelligent diagnostics, real-time monitoring, and data-driven decision-making. This review paper critically examines the integration of Ayurveda with AI and ML technologies to develop predictive healthcare systems aligned with the vision of Sustainable Bharat 2047. The study synthesizes findings from recent research published between 2019 and 2026 related to AI-driven healthcare, Prakriti assessment, predictive disease modeling, IoT-enabled monitoring systems, digital Ayurveda platforms, and sustainable healthcare ecosystems. The review identifies major technological approaches including Random Forest, Support Vector Machines (SVM), Deep Learning, Natural Language Processing (NLP), fuzzy logic, and sensor-based analytics for personalized healthcare prediction and disease prevention. Critical challenges including lack of standardized Ayurvedic datasets, ethical concerns, algorithmic bias, cultural sensitivity, interoperability, and data privacy are also examined. Finally, the paper proposes a conceptual framework integrating traditional Ayurvedic principles with intelligent digital technologies for future-ready healthcare systems.

Journal of Advanced Research in Applied Artificial Intelligence and Neural Network

Recent Advances in AI-Based Heart Disease Prediction: A Review of Machine Learning and Deep Learning Approaches

Predictive Healthcare Using Ayurveda and Machine Learning for Sustainable Bharat 2047