Comparative Study of Logistic Regression, Neural Network, and Deep Learning in Predicting Hypertension Risk

Hypertension is a major risk factor for cardiovascular diseases, and early detection is crucial for effective management. This study compares the predictive performance of three modeling techniques—Logistic Regression (LR), Neural Network (NN), and Deep Learning (DL)—in estimating the risk of hypertension. The dataset, obtained from Kaggle, consists of demographic and clinical variables with binary labels indicating the presence or absence of hypertension. Each model was trained and evaluated using RapidMiner, with performance assessed through accuracy and Root Mean Squared Error (RMSE). The results indicate that the Neural Network outperformed both Deep Learning and Logistic Regression, achieving the highest accuracy (99.88%) and the lowest RMSE (0.124). These findings suggest that shallow neural networks can provide reliable and efficient predictions for hypertension risk, sometimes even surpassing more complex deep learning architectures.