Kardiyovasküler hastalık tahmininin geliştirilmesi için entegratif makine öğrenmesi yaklaşımları: XGBoost ve ANFIS algoritmalarının karşılaştırmalı analizi

Cardiovascular diseases (CVDs) are the leading cause of death globally, underscoring the need for advanced detection and diagnostic methods to enhance patient outcomes. This study investigates the efficacy of two machine learning algorithms, XGBoost and the Adaptive Neuro-Fuzzy Inference System (ANFIS), in predicting heart disease across diverse datasets. Utilizing datasets from the UCI Machine Learning Repository, including Switzerland, Cleveland, Hungarian, Long Beach VA, and Statlog Heart, standard preprocessing techniques such as imputation, standardization, one-hot encoding, and SMOTEENN were applied to ensure consistent modeling conditions. Both models underwent extensive training and optimization. XGBoost excelled, particularly achieving 100% accuracy in the Switzerland and Statlog datasets, while ANFIS demonstrated its strength in modeling complex patterns, notably achieving perfect accuracy in the Cleveland dataset. Performance evaluations using accuracy, precision, recall, F1 score, F2 score, and ROC-AUC score highlighted XGBoost's consistent high precision and recall, vital for reliable CVD diagnosis. In contrast, ANFIS showed potential in clinical settings with its high F2 scores, emphasizing the reduction of false negatives. The study highlights the advantages of using advanced machine learning models like XGBoost and ANFIS in cardiovascular diagnostics, suggesting further research with larger and more varied datasets to refine these models and advance medical diagnostics using machine learning.