Classification of Heart Diseases Using Logistic Regression with Various Preprocessing Techniques

Machine learning (ML) based heart disease prediction has emerged as a crucial and fruitful field of study and application. They are used to analyze medical data, identify cardiac disorders, and make precise predictions about their presence or absence. Utilizing the effectiveness of LR (Logistic Regression) in combination with sophisticated preprocessing methods has emerged as an important strategy in the classification of heart-related diseases. By addressing data variability and differences in feature sizes, the use of decimal scaling and min-max normalization, in particular, improves the interpretability and flexibility of the model. By detecting complex underlying patterns and nonlinear interactions within the data, the use of Isomap (Isomeric Feature Mapping) normalization enhances the LRs (Logistic Regression) discriminative powers. This Scope of research has shown the possibility to produce higher classification results through a thorough review procedure that includes accuracy, precision, and recall criteria. Isomeric Feature Mapping along with LR gives best result with accuracy of 91%, precision of 0.89 and recall of 0.87 respectively. This proposed system is compared with the existing methods like Min-Max Normalization+ LR, and Decimal Scaling Normalization+ LR. The tool used for execution is python.