Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning integrated with the internet of things

This paper plans to develop an effective machine learning system integrated with the Internet of Things (IoT)
to predict the health insurance amount. IoT in healthcare enables interoperability, machine-to-machine com- munication, information exchange, and data movement that make healthcare service delivery effective. The
model includes three phases(a) Feature Extraction, and(b) Weighted Feature Extraction, and(c) Prediction. The
feature extraction process computes two statistical measures: First Order Statistics like mean, median, standard
deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics
like Kurtosis, skewness, correlation, and entropy. The prediction process deploys a renowned machine learn-
ing algorithm called Neural Network (NN). As the main contribution, the weighted feature vector is developed
here, where the weight optimally tuned by Modified Whale Optimization Algorithm (WOA). Also, the contribution relies on NN, where the training algorithm replaced with the same modified WOA for weight update. The modified WOA developed here is termed as Fitness dependent Randomized Whale Optimization Algorithm (FR-WOA). At last, the valuable experimental analysis using three datasets confirms the efficient performance of the suggested model.