Multi chronic disease prediction: A survey

Abstract

People today deal with a variety of illnesses as a result of their lifestyle choices and the environment. As a result, many people have chronic diseases that go untreated for long periods of time, imposing a tremendous impact on society. Therefore, predicting disease sooner is becoming a crucial duty. in order to systematically evaluate patients' future disease risks using their medical records. But for a doctor, making an accurate forecast based on symptoms is too challenging. The hardest task is making an accurate diagnosis of a condition. For this problem to be resolved, illness detection requires the use of deep learning and machine learning approaches. The amount of data in the medical sciences grows significantly every year. Earlier, health care for patient care has benefited from precise medical data analysis due of the development of information in the medical and healthcare areas. Prior identification and therapy are usually necessary to prevent chronic aeropathy from getting worse. Machine learning and deep learning algorithms are used to predict chronic diseases. Eight illness categories were our predictions. The Random Forest ensemble learning approach fared best overall. Finding the sickness prediction techniques with the highest accuracy and computation efficiency is the aim of this study.