Next-Generation Systems and Secure Computing

19Machine Learning Applications, Challenges, and Securities for Remote Healthcare: A Systematic Review

Arpan Adhikary^1*, Sima Das², Asit Kumar Nayek¹, Monojit Manna¹ and Rabindranath Sahu¹

¹Haldia Institute of Technology, Haldia, West Bengal, India

²Bengal College of Engineering and Technology, Durgapur, West Bengal, India

Abstract

Machine Learning (ML), a subset of Artificial Intelligence (AI), has shifted the computational paradigm to solve our day-to-day problems. Beside robotics, Natural Language Processing (NLP), predictions in different fields, and healthcare industries are growing within the scope of ML. It produces promising opportunities to track human activities, different disease diagnosis, prediction, and classification. Some sensor-based telemonitoring systems have been introduced to help disabled and elderly people to live normal lives. These sensors are used to collect real-time data for model training. However, storing real-time medical data needs proper security to provide confidentiality among users. Sensors are capable of producing a huge amount of information. Storing them to the cloud needs a lot of computational resources per user basis. Thus, minimal intelligence machine learning models can be used to manipulate the data on edge devices. We have discussed the challenges and future opportunities of ML models in the healthcare industry. Our objective for this study is to identify the recent problems faced by ML models and how these problems can be addressed. ...