Smart Healthcare System Management Using IoT and Machine Learning Techniques

Traditional health care system need to be smarter because of the increasing complicacy in health of human kind. The new technology like IoT and Artificial intelligence system has a big role in making it success. IoT, Cloud Computing, and AI have made the traditional healthcare system smarter. Improving medical care with IoT and AI. Combining IoT and AI provides healthcare new options. In this approach, AI and IoT can assist intelligent healthcare systems detect illness. This article uses AI and IoT convergence to discover heart disease and diabetes. The given model includes data collection, preprocessing, classification, and parameter adjustment. Wearables and sensors make it easier to collect IoT data, which AI can use to diagnose illness. The suggested technique for detecting illnesses uses CSO-CLSTM, based on Crowd Search Optimization. CSO fine-tunes the CLSTM model’s “weights” and “bias” to enhance medical data categorization. Outliers are removed using the isolation Forest (iForest) approach. CSO improves CLSTM’s diagnostics. Healthcare data proved CSO-validity. LSTM’s CNN2D now includes a new version of LSTM and a CSO features selection approach. Experiments utilising Heart and Diabetes data reveal that extension is correct.