Random Forest Classification in Healthcare Decision Support for Disease Diagnosis

The use of random forests Classification in medical decision support for diagnosing diseases is investigated in this study. Based on interpretivism, the study uses a design that is descriptive in nature, a deductive methodology, and secondary data to evaluate the interpretability, adaptability, and effect of the model on diagnostic accuracy. The findings indicate that performance metrics are promising and that methods such as SHAP values have improved interpretability. Random Forest's potential is emphasized through comparing it with traditional methods, and its adaptability is further highlighted by its generalization across various healthcare domains. Refinement opportunities are found through critical analysis, which includes dealing with skepticism and overfitting. Additional model optimization and joint creation of user-friendly interfaces are among the recommendations. Further research should concentrate on improving interpretability, examination scalability, evaluating real-world impact, and fine-tuning advanced hyperparameters.