A machine learning and blockchain based secure and cost-effective framework for minor medical consultations

Vikas Hassija; Rahul Ratnakumar; Vinay Chamola; Soumya Agarwal; Aryan Mehra; Salil S Kanhere; Huynh Thi Thanh Binh

doi:10.1016/j.suscom.2021.100651

A machine learning and blockchain based secure and cost-effective framework for minor medical consultations

Sustain Comput. 2022 Sep:35:100651. doi: 10.1016/j.suscom.2021.100651. Epub 2021 Dec 27.

Authors

Vikas Hassija¹, Rahul Ratnakumar², Vinay Chamola³, Soumya Agarwal¹, Aryan Mehra⁴, Salil S Kanhere⁵, Huynh Thi Thanh Binh⁶

Affiliations

¹ Department of CS and IT, Jaypee Institute of Information Technology, Noida, 201304, India.
² Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India.
³ Department of Electrical and Electronics Engineering & APPCAIR, BITS-Pilani, Rajasthan, 333031, India.
⁴ Department of Computer Science, BITS-Pilani, Rajasthan, 333031, India.
⁵ School of Computer Science and Engineering, UNSW Sydney, Australia.
⁶ School of Information and Communication Technology, Hanoi University of Science and Technology, Vietnam.

Abstract

With the ever-increasing awareness among people regarding their health, visiting a doctor has become quite common. However, with the onset of the COVID-19 pandemic, home-based consultations are gaining popularity. Nevertheless, the worries over privacy and the lack of willingness to assist patients by the medical professionals in the online consultation process have made current models ineffective. In this paper, we present an advanced protected blockchain-based consultation model for minor medical conditions. Our model not only ensures users' privacy but by incorporating a calculation model, it also offers an opportunity for consulting end-users to voluntarily take part in the consultation process. Our work proposes a smart contract based on machine learning to be implemented for the prediction of a score of a professional who consults based on various prioritized parameters. This is done by using word2vec and TF-IDF weighting to classify the question and cosine similarity scores for detailed orientation analysis. Based on this score, the patient is charged, and simultaneously, the responder is awarded ether. An incentivized method leads to more accessible healthcare while reducing the cost itself.

Keywords: Blockchain; Ethereum; Logistic regression; Machine learning; Minor consultations; NLP; Naive Bayes.