Double stage network inference system using k means clustering and fuzzy cognitive maps for cardiovascular disease diagnosis

Abstract

The diagnosis of cardiovascular disease (CVD) is crucial as it stands as a prominent cause of mortality and its risk factors are often modifiable. Early diagnosis is an essential component of overall CVD health management and prevention strategies. When diagnosing CVD, numerous uncertainties arise, including variations in symptoms among individuals and symptom overlap with other diseases. To deal such uncertainties in the diagnosis of CVD, a fuzzy logic based diagnostic system is required. While designing fuzzy inference system (FIS), optimizing the rule is challenging task. When the rules are optimized in the system, the time complexity can be minimized and performance of the system may also be effective. Since optimized inference system will be more efficient, accurate, interpretable and easier to maintain, the objective of the study is to design a novel hybrid double stage network inference system using $k$ means clustering, fuzzy cognitive maps (FCM) and Mamdani fuzzy inference system (MFIS) for diagnosing CVD. Initially, risk factors of CVD are categorized into modifiable and non-modifiable factors through $k$ means clustering. Then, most influencing risk factors are identified from the modifiable risk factors using FCMs. The rules are created by implementing double stage network by incorporating sub factors of most influencing factors and biological factors. Then, the obtained rules from the double stage network are integrated into a MFIS to determine the level of CVD. The system’s effectiveness is assessed using a real-time clinical dataset comprising 1250 CVD risks, employing performance metrics, receiver operating curve (ROC) analysis and statistical evaluations. The proposed system demonstrates an exceptional performance, achieving a 99.23% accuracy, 98.99% sensitivity, 95.76% specificity and 99.46% precision in detecting CVD. This piece of work suggested that the proposed technique serves as a valuable tool for diagnosing CVD risks.