Patient Sickness Prediction Using Machine Learning

The ability to predict a patient's sickness is essential to modern healthcare since it allows for timely treatments that can significantly improve patient outcomes. This work aims to predict the risk of specific illnesses using machine learning (ML) techniques based on large patient data. This project intends to build prediction models utilizing test results, lifestyle factors, and electronic health records (EHRs) to assist healthcare professionals in identifying at-risk patients and facilitating preventative healthcare treatments. The results of the study will enhance clinical judgment and patient care by applying efficient predictive analytics in the medical field. Keywords: At-Risk Patients, Disease Prediction, Electronic Health Records (EHRs), Machine Learning (ML)