Machine Learning-Based Approach for Predictive Analytics in Healthcare

1. Introduction

The internet era has resulted in an exponential increase in the volume of data generated in digital form. Due to these rapid growths in the generation of the data, data is collected at the rate of terabytes to petabytes from each application(Shastri et. al,2020). Data analytics is the process of analyzing the data to obtain meaningful insights from it. Usage of statistical techniques and machine learning(ML) approaches to predict future trends from historical data is known as predictive analytics. Machine learning (ML) is the science that enables computers to learn and predict their experiences without explicit programming. If a computer software can improve its performance due to past experience, this is called 'learning.' In contrast to artificial intelligence, machine learning is more restricted to data analysis. The use of techniques that allow computers to learn from data iteratively is machine learning(Salkuti et. al,2020). Predictive analytics is expanding its application in various sectors like Heath care, Bank, Education, Governmental organizations, Retail industry, Cybersecurity, Manufacturing, Insurance sectors, stock market, social media, and many more(Wang et. al,2018) Today predictive analytics is making more buzz in the area of the health care sector. Because of the massive amount of healthcare data is generated in the digital form. Processing and analyzing these data is becoming challenging for the medical practitioner to take effective decisions. Hence predictive analytics are making highlights in the healthcare sector which can convert these digital data in the form of clinical insights using an efficient ML model which helps the physician in providing better treatment for the patient with lesser cost. There are limitless advantages in the application of predictive analytics in the field of healthcare.