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All Journal Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen Jurnal Kesehatan Manarang Indiktika : Jurnal Inovasi Pendidikan Matematika Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Gentle Birth Excellent Midwifery Journal Jurnal Ilmu Komunikasi Balayudha (JIKOBA) Jurnal Teknik Informatika Andalas International Journal of Socio-Humanities (AIJOSH) Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Mitrasehat International Journal of Health, Engineering and Technology Ezra Science Bulletin MULTIPLE: Journal of Global and Multidisciplinary Jurnal Pengabdian Masyarakat Sevaka: Hasil Kegiatan Layanan Masyarakat
Rika Handayani
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Journal : International Journal of Health, Engineering and Technology

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Classification of Infertility Risk in Female Patients Based on Medical Record Data Using Naive Bayes Algorithm Fahruzi Sirait; Halimah Tusakdiyah Harahap; Nadya Fitriani; Rika Handayani; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology Vol. 2 No. 4 (2023): IJHET NOVEMBER 2023
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v2i4.274

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%. The main factors that most influence the classification of infertility include a history of reproductive disease, patient age, hormonal examination results, body mass index, and history of sexually transmitted infections. These findings indicate that the integration of the Naive Bayes algorithm into medical record data can be an effective solution for early detection of infertility clinically and support data-based decision making. This study also recommends increasing data and attribute coverage, as well as comparison with other algorithms for more optimal results in the future
Co-Authors Agus Susanto Ahmad Luthfi Aldri Frinaldi Ana Fitri Ramadani Andi Muhammad Yaqkin Andi Muhammad Yaqkin Padjalangi Azmi Islami Baginda Restu Al Ghazali Darul Ilmi Dhiant Asri Dinda Imroatul Azizah Dinda Salsabila Ritonga Dini Nur Fibriana Eka Ramadhani Putra Ekayanti Hafidah Ahmad Endang Masitoh W Erise fatwa amaliya Fahruzi Sirait Fitrianingsih, Julia Fitriyani Nasution Halimah Tusakdiyah Harahap Halimah Tusyadiah Harahap Herman, Sriyana Hisbul maulana Holifah Intan Lestari Intan nur Fitriyani Isnaeni Isnaeni Jangga Junaidi Junaidi Kashardi, Kashardi Mustamin Nadya Fitriani Nailatun Nailatun Nadrah Novica Jolyarni D Putri, Selfi Mahat Quratih Adawiyah Rani Darma Sakti Tanjung Risnawati, Risna Rizky Amelya Furqan Sabri Sanghati Sangahti Saparuddin Latu Sidiq Purnomo, Agus SRI WULANDARI Suhendro Suhendro Tamira Tansy Timbuleng Taufiq Ramadhan Yantimala Mahmud Yuriska Destania Yusril Iza Mahendra Hasibuan Zainuddin