Fuzzy Rule based Intelligent Cardiovascular Disease Prediction using Complex Event Processing

• URL: http://arxiv.org/abs/2409.15372v1
• Date: Thu, 19 Sep 2024 16:36:24 GMT
• Title: Fuzzy Rule based Intelligent Cardiovascular Disease Prediction using Complex Event Processing
• Authors: Shashi Shekhar Kumar, Anurag Harsh, Ritesh Chandra, Sonali Agarwal,
• Abstract summary: Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities. We propose a fuzzy rule-based system for monitoring clinical data to provide real-time decision support.
• Score: 0.8668211481067458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. According to the World Health Organization (WHO), primary risk factors include elevated blood pressure, glucose, blood lipids, and obesity. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities, often relying on predictive models trained on large datasets, which require intensive training. An intelligent system for CVDs patients could greatly assist in making informed decisions by effectively analyzing health parameters. Complex Event Processing (CEP) has emerged as a valuable method for solving real-time challenges by aggregating patterns of interest and their causes and effects on end users. In this work, we propose a fuzzy rule-based system for monitoring clinical data to provide real-time decision support. We designed fuzzy rules based on clinical and WHO standards to ensure accurate predictions. Our integrated approach uses Apache Kafka and Spark for data streaming, and the Siddhi CEP engine for event processing. Additionally, we pass numerous cardiovascular disease-related parameters through CEP engines to ensure fast and reliable prediction decisions. To validate the effectiveness of our approach, we simulated real-time, unseen data to predict cardiovascular disease. Using synthetic data (1000 samples), we categorized it into "Very Low Risk, Low Risk, Medium Risk, High Risk, and Very High Risk." Validation results showed that 20% of samples were categorized as very low risk, 15-45% as low risk, 35-65% as medium risk, 55-85% as high risk, and 75% as very high risk.

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