Classification of Infertility Risk in Female Patients Based on Medical Record Data Using Naive Bayes Algorithm

fahruzisirait; Halimah Tusakdiyah Harahap; Nadya Fitriani; Rika Handayani4; Baginda Restu Al Ghazali

Authors

fahruzisirait Information Systems, Faculty of Computer Science, Ika Bina Institute of Technology and Health
Halimah Tusakdiyah Harahap Midwifery, Faculty of Health Sciences, Ika Bina Institute of Technology and Health
Nadya Fitriani Midwifery, Faculty of Health Sciences, Ika Bina Institute of Technology and Health
Rika Handayani4 Midwifery, Faculty of Health Sciences, Ika Bina Institute of Technology and Health
Baginda Restu Al Ghazali Information Systems, Faculty of Computer Science, Ika Bina Institute of Technology and Health

Keywords:

Infertility, Data mining, Naïve bayes, Electronic medical records, Rapidminer

Abstract

Infertility is a reproductive health problem that has a significant impact globally, especially in developing countries such as Indonesia. This study aims to classify the risk of infertility in female patients at Rantauprapat Regional Hospital by utilizing the Naive Bayes algorithm based on electronic medical record data. The data used consisted of 500 medical records of female patients of childbearing age during the period 2019–2022, which had been processed and divided into training data (70%) and testing data (30%). The analysis and modeling process was carried out using the RapidMiner application without requiring programming skills. The results showed that the Naive Bayes model was able to classify the risk of infertility with an accuracy level of 86.7%, precision of 91.0%, recall of 93.2%, and F1-score of 92.1

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