Fuzzy Expert System for Early Heart Disease Diagnosis Using Mamdani Method in Web-Based System - ANANDA DWI RIZKYTA; Muhamad Bahrul Ulum; Fenina Adline Twince Tobing (ULTIMATICS, 2026)
Heart disease is one of the leading causes of death and is known as a silent killer because it often does not show clear symptoms in the early stages. Limited medical personnel and access to health services are obstacles to early detection. This study proposes the development of a web-based expert system that utilizes the Fuzzy Mamdani method to perform rapid and accurate early diagnosis of heart disease. The system is designed using variables such as Body Mass Index (BMI), blood pressure, medical history, smoking habits, psychological aspects, and common symptoms that have been validated by medical professionals. The Fuzzy Mamdani method was chosen for its ability to handle data uncertainty and produce decisions that resemble human reasoning. Development was carried out using the Extreme Programming method, which includes the stages of planning, design, coding, and testing. Testing results show that the system can provide accurate risk estimates and is easily accessible to the public via computers or mobile devices. This system serves as an early detection tool to raise awareness of heart health and encourage further medical examinations, not as a replacement for the role of a doctor.