Key result
Logistic regression model predicts heart disease risk with ~93% accuracy using standard clinical parameters.
Why the study?
Early diagnosis and reliable risk assessment of heart disease remain critical due to its global health burden and economic impact, necessitating effective predictive models.
Observational (n=270)
Logistic regression provides a highly accurate and interpretable predictive model for assessing heart disease risk based on standard clinical parameters.
Offers interpretable heart disease risk prediction; leaves open prospective validation before clinical use.
Heart disease remains a major global health concern and one of the leading causes of mortality, emphasizing the importance of early detection and reliable risk assessment.This research investigates the effectiveness of Logistic Regression (LR) in predicting heart disease risk using the Heart Disease Statlog dataset.Various clinical and demographic parameters including age, cholesterol levels, exercise-induced angina, and ST depression were examined to build a predictive model.The dataset was subjected to preprocessing, feature selection, and exploratory data analysis (EDA) before model training.Evaluation metrics such as accuracy (92.59%), precision (94.7%), recall (85.7%), and AUC-ROC (0.95) demonstrated strong model performance.The study underscores the interpretability of LR for medical diagnosis while recognizing its limitations in modeling complex non-linear patterns.Future studies may enhance prediction accuracy by employing advanced machine learning techniques and integrating real-time health monitoring systems.Overall, the findings support improvements in early disease detection, healthcare decision-making, and actuarial risk analysis in the health insurance sector.
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Latha et al. (2026) conducted an observational in Heart disease (n=270). Logistic Regression model was evaluated on Model prediction accuracy and AUC-ROC. A logistic regression model predicted heart disease risk using clinical parameters with 92.59% accuracy and an AUC-ROC of 0.95.
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