Cardiovascular disease continues to be the world’s number one killer, highlighting the critical necessity for precise, early diagnosis. 16 clinically significant characteristics are included in the dataset, including demographic, behavioral, and physiological factors that are important for determining the risk of long-term coronary heart disease. By using a modified sigmoid activation function that provides controlled scaling, shifting, and clipping of the input logits, the fundamental methodology focuses on improving the logistic regression model. This change is intended to stop numerical instability, like exponential overflow, which frequently occurs when training on high-magnitude or unbalanced data. By directly altering the activation function within the logistic regression framework, the innovation provides enhanced numerical stability without sacrificing interpretability, which is crucial for clinical applications. Stratified sampling was employed to train and evaluate the model, and standard performance metrics were utilized for its validation. The outcomes indicate that the proposed model improves upon baseline logistic regression (accuracy: 84.74%, F1-score: 0.118) by achieving an accuracy of 85.14%, F1-score of 0.104, and maintaining a comparable ROC-AUC of 0.70, indicating robustness in prediction stability.