Machine Learning for the Diagnosis and Prognosis of Chronic Illnesses

Abstract

An essential part of healthcare is disease prediction, which seeks to identify people who are at risk of getting certain diseases. Because of their superior capacity to sift through massive datasets in search of intricate patterns, machine learning algorithms have recently become useful instruments in the fight against illness prediction. The goal of this project is to make it easier for people to diagnose their own health problems using just their symptoms and precise vital signs. Due to excessive medical expenditures, many people put off taking care of their health, which can result in worsening symptoms or even death. Medical expenses can be overwhelming for people without health insurance. Using machine learning methods like ExtRa Trees, the suggested system provides a general illness forecast based on patients' symptoms. The algorithm provides a possible diagnosis based on the user's age, gender, and symptoms, suggesting that the user may be experiencing a certain illness. The system also suggests healthy eating and exercise routines to help lessen the impact of the condition, depending on how bad it is. Lastly, this article has shown a comparison examination of the suggested system using several algorithms including logistic regression, decision tree, and Naïve Bayes. The efficiency and accuracy of illness prediction are both enhanced by the suggested model.