Predictive Analysis from Patient Health Records Using Machine Learning

In the healthcare sector, predictive analysis deals with identifying potential health risks and diagnosing patients with the help of patient data-driven models. This development has many potential applications, including reducing the workload on healthcare workers by accelerating the diagnostic process and predicting the likelihood of patients developing specific diseases. Predictive analysis can be extremely useful in Cardiology, as early risk detection would greatly help healthcare professionals to ensure timely treatment and care. In our study, we utilized machine learning techniques and approaches to analyze patient health data and predict the likelihood of Cardiovascular Diseases. A machine learning algorithm was developed that produced positive results using feature selection and ensemble techniques, and an accuracy rate of 90.24 percent was achieved. These results show us the potential of predictive analysis in cardiology and the importance of using patient data to develop more efficient treatment methods.