Estimation of Chronic Illness Severity Based on Machine Learning Methods

Chronic diseases are diseases that last one year or more and require a continuous medical care and monitoring. Based on this point, a dataset from an APP called Flaredown helps patients of chronic disease improve their symptoms and conditions. In this study, an illness severity-level model was proposed to give the patient an alert to his or her health condition into three different levels according to their severity. Personal information, treatment conditions, and dietary conditions were analyzed by a statistical measure, TD-IDF. Seven different machine learning models were used and compared to generate the illness severity-level model. The results revealed that the XGBoost model with a F-1 score of 0.85 and LightGBM model with a F-1 score of 0.84 have the best performance. We also applied feature selection and parameter tuning for these two models to attain better performance, and the final best F-1 scores achieved by the XGBoost model and LightGBM model were both 0.85. Sensitivity analysis has shown that the treatment feature and symptom feature have important effects on the classification of the illness severity-level. Based on this, a fusion model was designed to study the data and the final accuracy of the fusion model was 93.3%. Thus, this study provides an effective illness severity-level model for a reference and guidance for the management of high-risk groups of chronic diseases. Patients may use this illness severity-level model to self-monitor their illness conditions and take proactive steps to avoid deterioration of their illness and take further medical care.