Heart Disease Prediction Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model with Feature Extraction and Data Pre-Processing

The new approach came from the research, and its main goal was to create a more accurate model for predicting heart diseases, which would utilize the very powerful combination of data preprocessing, feature extraction, and the latest Adaptive Neuro-Fuzzy Inference System (ANFIS). The application of Principal Component Analysis (PCA) and Mutual Information (MI) not only helped in the reduction of dimensionality and the advancement of feature relevance but also gave support to the model’s learning power. The accuracy achieved through the method proposed was 95.5%, which was supported by 96.0% recall and 94.8% precision as opposed to traditional machine learning and ensemble approaches. The blending of neuro-fuzzy methods contributes to the improvement of diagnosis performance, user-friendliness, and reliability against competing systems, thereby making such a system applicable in diagnosing heart diseases in the medical field.