EFFICIENT HEART DISEASE PREDICTION MODEL THROUGH RETE ALGORITHM-BASED RULE MATCHING

Monica Oyiri Dike; Euphemia K Okeiyi; Daniel Azemobor; Chinagorom Ituma; Gift Adene

EFFICIENT HEART DISEASE PREDICTION MODEL THROUGH RETE ALGORITHM-BASED RULE MATCHING

Monica Oyiri Dike, Euphemia K Okeiyi, Daniel Azemobor, Chinagorom Ituma, Gift Adene

Abstract

Cardiovascular diseases remain a leading cause of global mortality, necessitating efficient and accurate diagnostic tools. Traditional diagnosis methods are time-consuming, costly, and heavily dependent on medical professionals, leading to delays in treatment. This study presents a heart disease prediction model leveraging the Rete Algorithm, designed to enhance diagnostic accuracy and efficiency. The Structured Systems Analysis and Design Methodology (SSADM) and Object-Oriented Analysis and Design Methodology (OOADM) were employed for system development, ensuring flexibility, modularity, and seamless integration. The system utilizes PHP for the user interface, MySQL for database management, and Python for data processing and decision-making accuracy. Preprocessed medical datasets were analyzed using a decision tree approach to maintain data integrity, achieving an accuracy rate of 80% during evaluation. The results demonstrate that the Rete Algorithm significantly improves real-time diagnosis, reduces the workload on medical professionals, and provides a cost-effective solution for early heart disease detection.

Keywords

Rete Algorithm; Cardiovascular disease; Decision making; Healthcare

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References

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