An Expert System to Monitor and Risk Assessment of Chronic Disease Patients Using FTOPSIS

Chronic diseases are the primary causes of mortality and impairment in Bangladesh, as well as the leading cause of healthcare expenses. Doctors frequently diagnose chronic disease patients based on symptoms. Following the test reports, patients are then prescribed medication for a certain time. During this time, it is imperative to regularly monitor the patient’s condition. However, these illnesses are expensive to diagnose. In this particular scenario, Fuzzy TOPSIS has the potential to be an effective solution. The goal of using Fuzzy TOPSIS with multi-criteria decision-making (MCDM) in patient follow-up is that instead of depending entirely on diagnosis, we have combined logic-based premises and outcomes based on statistics. This study presents an expert system that can determine a chronic disease patient’s weekly health state. Additionally, the patient’s health is monitored daily to track any changes that may occur. We have selected five distinct chronic conditions and 19 people who are afflicted with five distinct chronic diseases. The laboratory criteria of these diseases are chosen by two feature selection methods. Multiple decision-makers were explored for each condition. In response to the data collection, a new decision matrix has been constructed. The newly created decision matrix is then normalized and weighted. Following this, the patient’s weekly health condition is determined based on the closeness coefficient scores. Finally, variance is utilized to determine the variation from day to day. As a consequence of this, one can take the appropriate measures for further treatment, which reduces both expenses and the amount of time required.