Improving Heart Disease Diagnosis through Data-Driven Machine Learning Models

DOI

10.2991/978-94-6239-654-8_32How to use a DOI?

Keywords

machine learning technique; cardiovascular disease; Decision tree classifier; multilayer perceptron (MLP); XGBoost

Abstract

Since cardiovascular diseases (CVDs) are the world's leading cause of mortality, early and precise diagnosis is crucial. Conventional diagnostic techniques take a lot of time and are prone to human error. Machine learning (ML) is a promising way to increase diagnostic accuracy as electronic health records (EHRs) and massive medical data become more prevalent. The supervised learning models Decision Tree (DT), XGBoost (XGB), and Multilayer Perceptron (MLP) are combined with k-modes clustering for categorical data preprocessing in this study's hybrid machine learning framework. Eighty percent of the 11,000 records of patients from Kaggle are used for training, and the remaining twenty percent is used for testing. Cross-validation guarantees model robustness, while GridSearchCV is employed for hyperparameter optimization. The results demonstrate how ML, and particularly MLP, can improve diagnostic systems, facilitate prompt decision-making, lower errors, and even save lives when used in clinical contexts.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Manoranjani
AU  - S. Arulselvi
AU  - B. Karthik
PY  - 2026
DA  - 2026/04/24
TI  - Improving Heart Disease Diagnosis through Data-Driven Machine Learning Models
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 381
EP  - 395
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_32
DO  - 10.2991/978-94-6239-654-8_32
ID  - Manoranjani2026
ER  -