Heart Disease Classification Using Random Forest and Fox Algorithm as Hyperparameter Tuning

Heart disease remains the leading cause of death worldwide, making early and accurate diagnosis crucial for reducing mortality and improving patient outcomes. Traditional diagnostic approaches often suffer from subjectivity, delay, and high costs. Therefore, an effective and automated classification system is necessary to assist medical professionals in making more accurate and timely decisions. This study aims to develop a heart disease classification model using Random Forest, optimized through the FOX algorithm for hyperparameter tuning, to improve predictive performance and reliability. The main contribution of this research lies in the integration of the FOX metaheuristic optimization algorithm with the RF classifier. FOX, inspired by fox hunting behavior, balances exploration and exploitation in searching for the optimal hyperparameters. The proposed RF-FOX model is evaluated on the UCI Heart Disease dataset consisting of 303 instances and 13 features. Several preprocessing steps were conducted, including label encoding, outlier removal, missing value imputation, normalization, and class balancing using SMOTE-NC. FOX was used to optimize six RF hyperparameters across a defined search space. The experimental results demonstrate that the RF-FOX model achieved superior performance compared to standard RF and other hybrid optimization methods. With a training accuracy of 100% and testing accuracy of 97.83%, the model also attained precision (97.83%), recall (97.88%), and F1-score (97.89%). It significantly outperformed RF-GS, RF-RS, RF-PSO, RF-BA, and RF-FA models in all evaluation metrics. In conclusion, the RF-FOX model proves highly effective for heart disease classification, providing enhanced accuracy, reduced misclassification, and clinical applicability. This approach not only optimizes classifier performance but also supports medical decision-making with interpretable and reliable outcomes. Future work may involve validating the model on more diverse datasets to further ensure its generalizability and robustness.