Designing an Expert System Using Data Mining Algorithms to Detect Infertility

Diagnosis of infertility is essential. In this research, a new method for diagnosing infertility is presented. In the proposed method, we use the best parameters of categorization methods to train classificatory methods train the best way and provide better accuracy to users. In the proposed method, we try to integrate the results based on the maximum number of votes in order to increase the accuracy of the proposed method. These algorithms analyze large volumes of medical data and patient histories to discover hidden patterns and relationships between symptoms, tests, and results. Overall, this topic focuses on designing an intelligent system that examines patient data (symptoms, tests, medical results, etc.), utilizes data mining algorithms to extract knowledge and patterns, and can automatically and accurately diagnose whether an individual or couple is affected by infertility. The main goal of this system is to reduce costs and improve diagnostic accuracy compared to traditional methods. This research aims to design an expert system for infertility diagnosis using data mining algorithms. The purpose of this system is to analyze patient data and extract effective patterns for disease detection. Using this approach reduces costs and increases the accuracy of infertility diagnosis. Moreover, this system can assist doctors in making faster and more accurate decisions.