Fuzzy Logic and Machine Learning Integration: Enhancing Healthcare Decision-Making

Abstract

The escalating healthcare costs in contemporary society have raised significant concerns. Identifying medical risks efficiently is crucial for reducing treatment expenses and improving overall health outcomes. However, the current disease risk assessment process involves multiple tests and requires medical professionals' expertise, leading to time-consuming and expensive procedures. In response to these challenges, the current role of machine learning in healthcare holds promise by offering efficient solutions for disease risk assessment, potentially streamlining processes, and contributing to cost reduction while improving health outcomes. However, the classification of medical diseases using machine learning (ML) algorithms presents challenges due to the presence of incomplete, uncertain, and inaccurate data. This research paper conducts a comprehensive survey of prior studies in the application of ML techniques for disease diagnosis, emphasizing the need for a system capable of integrating both linguistic and numeric inputs to enhance the diagnostic process's robustness. The study aims to surpass merely improving clinical outcomes by focusing on enhancing diagnostic accuracy, optimizing patient care, and resource utilization. It further explores machine learning (ML) techniques for disease diagnosis, introducing a hybrid MLfuzzy logic (FL) model evaluated on five healthcare datasets related to diabetes, heart stroke, heart failure, and body fat predictions. The empirical findings and evaluations are conducted using the Python 3.8.3 environment with Jupyter Notebook. Seven existing ML algorithms, alongside the proposed hybrid Fuzzy-PCA-SVM model, are employed on all datasets. To evaluate the model's effectiveness, various performance standards, including accuracy, precision, F1-score, and recall, have been considered. The results demonstrate that by leveraging the benefits of both SVM and FL systems, the suggested hybrid model outperforms other ML models. The study not only underscores the significance of integrating linguistic and numeric inputs in disease diagnosis but also envisions future research focused on real-world datasets and improved feature selection techniques for continued advancements in healthcare analytics.