Efficient Early Detection of Patient Diagnosis and Cardiovascular Disease using an IoT System with Machine Learning and Fuzzy Logic

: Rising healthcare challenges, particularly undiagnosed heart disease due to subtle symptoms and limited access to diagnostics, necessitate innovative solutions. This study introduces an innovative Internet of Things (IoT)-based system for early detection, leveraging the strengths of both fuzzy logic and machine learning. By analyzing patient-specific data such as heart rate, oxygen saturation, galvanic skin response, and body temperature, our system utilizes fuzzy logic to evaluate potential disease symptoms, enabling self-diagnosis under medical supervision. This personalized approach enables individuals to monitor their health and seek prompt medical attention as needed. Additionally, we train multiple machine learning algorithms (Decision Tree, KNN, SVM, Random Forest, Logistic Regression) on the well-established Cleveland heart disease dataset. Among these, Random Forest achieved the highest accuracy (82.6%), precision (81.5%), recall (83.7%), and F1-Score (82.5%), showcasing its e ff ectiveness in predicting cardiovascular disease. This unique blend of fuzzy logic for personalized symptom assessment and machine learning for CVD prediction presents a new method for early diagnosis. While promising, further validation through large-scale clinical trials is essential. Ultimately, this system underscores the significance of integrating AI with medical expertise for optimal patient care, providing a potential pathway to improved health outcomes and enhanced accessibility to early detection of cardiovascular disease.