Enhancing Heart Disease Prediction Using Artificial Neural Network with Preprocessing Techniques

Heart disease and other cardiovascular disorders continue to be the most prevalent cause of death. ML (Machine Learning) algorithms in particular have shown promise in forecasting for early identification and prevention. Using innovative preprocessing methods like Z-score normalization, IQR outlier handling, and Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, the present research investigates the use of ANN (Artificial Neural Networks) in the early detection of cardiovascular disease. When compared with various preprocessing techniques, SMOTE and ANN regularly exceed them in terms of precision, sensitivity, and specificity, according to the results of the study. The balanced illustration of both positive and negative cases in the synthesized dataset gives the NN (Neural Network) a more thorough learning experience. Since there are fewer false negatives (greater sensitivity) and false positives (more specificity) due to the ANN model’s increased accuracy for forecasting heart disease, there are fewer false positives as well. From the results obtained proposed SMOTE+ANN produces Accuracy of 91%, Specificity of 0.86 and Sensitivity of 0.91. The tool used is Jupyter Notebook and language used is python.