WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 25, 2026
Comparative Analysis of Machine Learning Algorithms for Heart Disease Classification and Integration with a Custom GPT-based Chatbot
Authors: ,
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Abstract: This study investigates the integration of traditional machine learning (ML) algorithms with a custom
GPT chatbot to enhance heart disease classification. Five ML models—K-Nearest Neighbors (KNN), Logistic
Regression, Support Vector Machine (SVM), Random Forest, and XGBoost—are applied to a curated dataset
for intent detection. After rigorous preprocessing and model training, performance is evaluated using standard
classification metrics. The best-performing model is then used to drive the logic of a fine-tuned Custom GPT
model, improving contextual understanding and response generation. The paper also details the GPT customization
pipeline and highlights the improvements observed through hybrid ML-GPT integration.
Keywords:
Heart disease classification, Machine learning, XGBoost, GPT chatbot, Intent detection, AI in healthcare, Custom GPT, KNN, SVM, Random Forest, Logistic Regression
Pages: 259-267
DOI: 10.37394/23205.2026.25.25