| Sumario: | Due to the development of Internet of Things (IoT) and related devices in the healthcare sector, diverse set of medical applications and services becomes feasible. The massive quantity of healthcare data produced by IoT devices requires cloud computing platform for handling it. This paper presents a new IoT and Cloud Enabled Disease Diagnosis and Prediction Model using Teaching and Learning based Optimization (TLBO) Algorithm with Kernel Extreme Learning Machine (KELM), named TLBO-KELM model. The proposed model initially performs data collection, where the acquisition of healthcare data takes place using three sources such as IoT devices, benchmark data repositories, and medical records. Then, the TLBO-KELM model gets executed to diagnose and predict the existence of diseases using the patient data. Besides, the application of TLBO algorithm in KELM helps to effectively tune the parameters for achieving better classification performance. The performance of the TLBO-KELM model has been tested against benchmark pimaindian diabetes dataset. The simulation outcome ensured that the TLBO-KELM model has outperformed the earlier models in a significant manner.
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