Title: Prediction of cardiovascular disease using intelligent nextgen machine learning algorithmic breakthroughs

Authors: M. Rohini; S. Oswalt Manoj

Addresses: TIFAC-CORE in Cyber Security, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India ' Sri Krishna College of Engineering and Technology, Coimbatore, India

Abstract: Cardiovascular disease (CVD) is a prevalent and life-threatening condition that affects middle-aged and elderly individuals, leading to severe complications due to unhealthy lifestyles. The goal of the study was to train the prospective machine learning model that introduces a novel approach by integrating generative pre-trained models, combined with attention mechanisms, to refine feature selection to detect cardiac disease at an early stage. The analysis of clinical data demands distinct challenges in the context of generative model learning due to data complexity and the diverse nature of disease markers. Addressing these, the proposed study significantly improved the predictive accuracy by refining the model's ability to recognise patterns specific to cardiac conditions. The gradient boosting (GB) algorithm emerged as the most effective optimal predictor, with 97.86% accuracy, 98.52% sensitivity, and 99.72% ROC for CVD classification.

Keywords: machine learning; ML; gradient boosting; GB; logistic regression; LR; cardiovascular disease; CVD; gene prediction.

DOI: 10.1504/IJIIDS.2026.152769

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.2, pp.231 - 255

Received: 27 Sep 2024
Accepted: 08 Jan 2025
Published online: 10 Apr 2026 *

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