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

Abstract

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 communication, 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 learning 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.

KEYWORDS:

• Medical insurance
• Internet of Things
• prediction
• weighted feature vector
• Neural network
• Whale Optimization algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Subrata Chowdhury

Subrata Chowdhury had received his BCA from the Punjab Technical University 2012. He pursued his MCA from the VIT Vellore from the year 2015, currently he has been pursuing his PhD research work from the VISTAS Pallavaram, he had published papers in the international journals. He is associated with the international journals previously.

P. Mayilvahanan

P. Mayilvahanan received his BSc on computer science from Bharathidasan in 1990, in 1992 he received his MSc from Bhararthidasan, he received his MPhil from Alagappa University in computer science in the year 2005, he completed his ME from the Anna University in computer science from the year of 2007, he completed his Phd from the Vels University. He has been the Lecturer for the AMMACE Kancheepuram, he also been the lecturer for the Hindustan college and the Vellamal college. He had published many Research papers in renowned International journals more than 20 papers, he is the supervisor for so many research scholar. He had written a book on cloud computing. He is now holding the chair of research coordinator and the Professor of the Vels University. He has a record of 23 years of teaching experience.

Ramya Govindaraj

Ramya Govindaraj received her BTech (IT) from Adhiparasakthi college of Engineering under Anna University, India in 2006, MTech (IT) from VIT Vellore, India in 2009. Her subject interest includes picture language in formal theory, web applications, networking, programming languages. She has published more than 15 papers in international journals. She is currently working as a Assistant Professor (senior) in School of Information Technology, IT – Vellore, India. She is Life time member of CSI. She has more than 8 years of teaching experience.