Developing A Machine Learning Model to Predict Medication Adherence in Chronic Disease Management

Abstract

In the healthcare industry, chronic disease prediction is crucial. It is crucial to diagnose the illness early. Large amounts of data are generated in computer science as a result of significant technological advancements. Many medical databases are created as clinical information networks advance. Data mining, the process of managing vast amounts of diverse data and extracting insights from it, has emerged as a crucial area of study. Early illness detection, patient treatment, and community services from huge data creation in the biomedical and healthcare communities are all benefited by the accurate analysis of medical data. Nowadays, one of the main areas of research is the management and extraction of knowledge from vast amounts of diverse data. Accurate processing of health data helps the biomedical and healthcare communities by improving patient care, early illness detection, and community services. However, analytical precision is reduced if medical data is not sufficiently consistent. For the domains of biomedical pattern recognition and master learning, the perception and diagnosis of chronic disease are guaranteed to be consistent. Additionally, the decision-making approach's goal is pushed. The study of high-dimensional, multi-modal biomedical data can be effectively addressed by machine learning. In computer science, chronic disease prediction is crucial. Early detection and prediction of chronic disease is crucial. The dataset for chronic obstructive pulmonary disease is used for analysis by the suggested model. Using supervised machine learning techniques such as Random Forest, Multiplayer Perceptron, Logistic Regression, Stochastic Gradient Descent, and XG boost, we provide a chronic obstructive pulmonary disease prediction system. Next, we examine classification techniques for predicting chronic diseases using a variety of criteria, such as accuracy, precision, sensitivity, ROC, and AUC.