Prediction Model for the Healthcare Industry Using Machine Learning

Abstract

The role of machine learning in health care in emerging times, the field of research is industry. In machine learning, there are various forms of learning, including supervised, unsupervised, and reinforcement learning. These strategies are necessary to discover previously unknown relationships in data that are beneficial to society. In predictive modeling, historical data are used to predict a result variable. The uses of machine learning in medical care are turning into a benefit for disease identification and diagnostics. The healthcare industry can benefit from machine learning’s capacity to assist in the intelligent analysis of huge amounts of data. Different methods of machine learning, including supervised, unsupervised, and semi-supervised, reinforcement learning for health care, such as SVM, KNN, K-Mean clustering, neural network, and decision tree, provide varying levels of accuracy, precision, and sensitivity. The area of machine learning (ML) is on the rise. The purpose of machine learning is to automatically discover patterns and reason with data. ML offers tailored therapy-dubbed precision medicine. Health care has benefited from the application of machine learning approaches. Within a few years, machine learning will alter the healthcare industry.