Development of Medical IOT System for the Prediction of Heart Disease

There are many uses for the Internet from Things (IoT) in fields like industry, healthcare, and farming. The worldwide popularity of the Internet about Medical Things (IoMT) has grown thanks to a big increase in personal tech, especially in health tracking systems. IoMT is very important for lowering death rates by finding diseases early, but it is very hard for it to predict heart illnesses from clinical records. Heart disease detection has been the subject of studies in the past, but their accuracy is still not great. In response, a new IoMT framework is suggested for predicting heart disease. This framework uses modified Salp Cluster Optimizer (MSSO) alongside an Adaptive Fuzzy Inference System for Neuro-Fuzzy (ANFIS). The Levy flight method makes the MSSO-ANFIS model better at searching, going beyond the limits imposed by conventional gradient-based discovering in ANFIS. Heart disease can be predicted by looking at medical records that include things like heart rate, age, sex, difficulty breathing, cholesterol, and blood sugar. The suggested model does a better job than others at classifying sensor data using MSSO-ANFIS. It achieves an impressive 99.45% accuracy and 96.54% precision. This program proves that MSSO-ANFIS is good at predicting diseases and shows that it is better than other methods.