Chronic Disease Prediction Using Machine Learning

Abstract

Abstract: Technological advancement, including machine learning, has a significant impact on health by allowing for more accurate diagnosis and treatment of various chronic diseases. Accurate prediction is critical in the biomedical and healthcare communities for determining the risk of disease in patients. The only way to overcome chronic disease mortality is to predict it earlier so that disease prevention can be implemented. Such a model is a Patient's requirement for which Machine Learning is highly recommended. However, a doctor finds it difficult to make an exact forecast based just on symptoms. The most challenging task is making an accurate diagnosis of a disease. Data mining is crucial in helping to predict the sickness and solve this issue. Based on a dataset for chronic diseases from the UCI machine learning data warehouse, this study assesses chronic diseases using machine learning techniques. In order to create accurate prediction models for various chronic diseases using data mining approaches, we employ datasets for heart disease, kidney disease, cancer disease, and diabetes disease. To increase accuracy and shorten training time, the dataset's most pertinent features are chosen. The system evaluates the user's symptoms as input and outputs the likelihood that the disease will occur. The implementation of Logistic Regression is used to predict disease. Prediction of diseases like diabetes, heart disease, cancer, and kidney disease using logistic regression, random forest, and decision trees are performed. Different models, methodologies, and algorithms are utilized to forecast and analyses each chronic disease. The study includes a conceptual model that includes the prediction of the majority of chronic diseases.