ABSTRACT
At present, Internet of Things (IoT) and cloud computing (CC)-based healthcare diagnosis models have been presented to monitor, predict, and diagnose the diseases. During medical data classification, most of the real-time datasets include some outlier or noise, which results into class imbalance problem. To resolve this issue, this study develops a new fuzzy support vector machine (FSVM) with Synthetic Marginal Oversampling Technique (SMOTE) model called SMOTE-FSVM for class imbalance problem in IoT and cloud-based disease diagnosis. The proposed SMOTE-FSVM model initially undergoes data collection using IoT devices connected to patients and transmits the data to cloud server. Then, SMOTE-based upsampling of marginal data samples takes place by the generation of synthetic data. Finally, the medical data classification process is carried out using FSVM model. The proposed SMOTE-FSVM model has been assessed using PIMA Indians Diabetes dataset and the performance is investigated under distinct performance measures. The simulation results indicated that the SMOTE-FSVM model has offered a maximum precision of 91.47%, recall of 89.63%, accuracy of 85.52%, and F score of 90.45%.
