Evaluating Machine Learning Approaches for Heart Disease Prediction with SPECT data: A Comparison between Logistic Regression and Random Forest Models

Abstract

The integration of machine learning with medical imaging has the potential to revolutionize diagnostic processes. This study compares the efficacy of Logistic Regression (LR) and Random Forest (RF) models in predicting heart disease using SPECT imaging data. Utilizing a dataset from Kaggle, the study involved preprocessing steps including data standardization and feature selection with SelectKBest and Recursive Feature Elimination (RFE). The models were trained and validated on separate datasets, with performance assessed through accuracy, sensitivity, and specificity metrics. Findings revealed that the LR model achieved a slightly higher accuracy of 71.12% compared to the RF model's 70.59%. Both models demonstrated identical sensitivity, but the LR model excelled in specificity, indicating better performance in identifying non-diseased cases. This comparative analysis underscores the potential of machine learning models in enhancing the accuracy and reliability of heart disease diagnosis from SPECT images, suggesting further exploration into multimodal approaches for improved predictive capabilities