Revolutionizing heart attack prognosis: Introducing an innovative regression model for prediction

Objective:

Heart attack prediction using machine learning is crucial for preemptive action and personalized healthcare. This research aims to predict heart attacks by employing machine learning in healthcare using a diverse range of patient data-including demographic, lifestyle, and physiological factors, which helps to create robust and generalizable predictions. Besides this, various models that balance accuracy with interpretability have been presented, emphasizing early detection and proactive intervention. It is expected that this cross-disciplinary approach will underline the role of machine learning in the mitigation of the heart disease burden and optimization of resources spent on healthcare.

Methods:

This study explores the application of machine learning techniques for predicting heart attack risk using structured clinical data. A range of classification models — Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) — were selected based on their proven effectiveness in prior healthcare prediction studies and their balance between accuracy and interpretability. The methodology involved comprehensive data preprocessing, class imbalance handling, and hyperparameter tuning to optimize model performance. Performance metrics included Accuracy, Precision, Recall, F1-score, and AUC-ROC. Exploratory Data Analysis (EDA) was conducted to assess the role of variables such as BMI, age, and glucose levels in predicting stroke, a proxy used for heart attack due to dataset limitations.

Results:

The SVM and LR models achieved the highest accuracy (95.08%), followed by RF (94.86%) and DT (91.46%). Despite high accuracy, key challenges were observed:

Class Imbalance: Only 249 cases in the dataset represented positive stroke outcomes, resulting in poor recall for minority class predictions. This reduced the model’s sensitivity to actual stroke cases, a significant limitation in clinical scenarios where false negatives can be life-threatening.

Data-Label Inconsistency: Although the study is framed as predicting heart attacks, the dataset pertains to stroke prediction. This misalignment creates confusion in the clinical relevance of the findings and weakens the generalizability of the models for heart attack risk assessment.

Lack of Model Interpretability in Practice: Though LIME and SHAP were cited as tools to ensure model transparency, they were not implemented or evaluated. This limits clinicians’ trust in the model’s predictions—an essential factor for real-world adoption.

Conclusion:

This research shows how machine learning can play a meaningful role in improving how we predict heart attacks and ultimately help improve patient care. The results demonstrated that even well-known models like Support Vector Machine and Logistic Regression can perform very well when applied to structured health data. It also became clear that everyday variables — such as age, BMI, glucose levels, and smoking habits — carry important signals for assessing cardiovascular risk. But while the models achieved high accuracy, the study also revealed that performance alone is not enough for real-world use. For machine learning to be truly useful in healthcare, models need to handle imbalanced data properly, offer transparent and understandable predictions, and stay aligned with clinical needs. This work not only highlights the potential of AI to transform predictive healthcare but also reminds us of the practical challenges that must be addressed along the way. Clear goals, interpretable results, and thoughtful integration into clinical practice are all key to making these tools safe, effective, and trusted by healthcare professionals.