Machine Learning Models for Early Disease Detection: A Big Data Approach to Personalized Healthcare

Abstract

Healthcare is one of the vast and most crucial industries in this world. In this era, the healthcare industry is producing a huge amount of data every day. Everybody’s life is captured by mobile devices/smart gadgets and smartwatches. Many records of human beings such as activities, heartbeat monitoring, running count, calorie count, number of steps, sleep patterns, daily mood, health status, stress monitoring, symptoms of diseases, blood pressure, temperature, and heart conditions are stored in the form of data. Predicting health issues automatically before noticing other symptoms is the need for the industry. A person’s health record data is analyzed using machine learning models to predict the chances of being affected by diseases automatically in a few seconds. The healthcare datasets that are used to build the machine learning models are completely clean, and no preprocessing is required.

In today’s world, automated prediction of diseases in an individual is progressing at a faster pace. Everyone is busy with their life and other needs; nobody has time to visit the hospital for a check-up every day. Therefore, every person needs a system that is capable of predicting diseases automatically in a matter of seconds in this busy world. ML techniques are used to analyze datasets containing a person’s health records and other necessary information based on which disease can be predicted. To predict diseases in an individual, supervised machine learning models such as logistic regression, decision tree, random forest, support vector machine, and naive bayes are applied. In many countries, mobile applications are developed for the prediction of diseases based on symptoms in an individual. Healthcare experts are consulted for prediction in these applications. By contrast, the dashboard predicted in this paper using machine learning models automates disease prediction completely with the highest accuracy and performance compared to the existing applications.

Machine learning has evolved into a miraculous tool for the healthcare industry. Many organizations are dedicated to the prediction of diseases and their diagnosis such as diabetes, liver diseases, lung cancer, heart disease, breast cancer, and more diseases using machine learning models. The performance of early disease diagnosis is getting better every day, and researchers are focused on studying people’s health records and enabling the system to predict diseases automatically. Compared to sharp models, high-performing deep learning models for personalized healthcare are not used widely as many healthcare industries have a huge number of unattended data. Elderly individuals and the common public have little or no technical knowledge and knowledge regarding the latest technologies, and ML applications may not specify.