Application of Machine Learning Algorithms in Early Detection of Diseases Using Medical Data

Irsadul Husna; Aisa Putri; Ton Kiat

Authors

Irsadul Husna Universitas Islam Negeri Mahmud Yunus Batusangkar Author
Aisa Putri Universitas Islam Negeri Mahmud Yunus Batusangkar Author
Ton Kiat Assumption University, Bangkok Author

Keywords:

Disease Detection, Machine Learning, Medical Data

Abstract

Early disease detection remains a major challenge in the medical field, especially when time constraints and limited resources lead to delayed diagnoses. This study aims to analyze the effectiveness of machine learning algorithms in early disease detection using patient medical data, identify the most accurate algorithms, and evaluate their implementation through field data. A qualitative approach with a case study method was employed, involving interviews, observations, and documentation within a single healthcare institution. Data analysis was conducted through reduction, presentation, and verification, with validation ensured through source triangulation. The findings reveal that algorithms such as XGBoost, Random Forest, and SVM perform highly in disease classification, with XGBoost reaching up to 89% accuracy in detecting heart disease risk. Another noteworthy finding is that the quality and consistency of medical data significantly influence prediction accuracy. Furthermore, the integration of artificial intelligence systems with electronic medical records still faces technical challenges but has shown a positive impact on service efficiency and medical decision-making. This research contributes meaningfully to both theoretical and practical advancements in medical science and opens future opportunities for data-driven predictive systems in healthcare. Hence, machine learning proves to be a promising approach in supporting the digital transformation of healthcare services

References

Anas, M. A. S., & Sofiya, I. (2022). Analisis dan perancangan sistem informasi pengolahan data rekam medis berbasis WEB di puskesmas XYZ. INFOTECH: Jurnal Informatika & Teknologi, 3(2), 82–91. https://doi.org/10.37373/infotech.v3i2.350

Basyarudin, B. (2022). Aspek Yuridis Rekam Medis Elektronik Dijadikan Alat Bukti Apabila Terjadi Kesalahan Pelayanan Kesehatan. Jurnal Cakrawala Ilmiah, 1(12), 3495–3510. https://doi.org/10.53625/jcijurnalcakrawalailmiah.v1i12.3212

Chen, X., Liu, J., & Yin, W. (2024). Learning to optimize: A tutorial for continuous and mixed-integer optimization. Science China Mathematics, 67(6), 1191–1262. https://doi.org/10.1007/s11425-023-2293-3

Gavrilova, E. A. (2023). Pathological Athlete’s Heart: The Development of the Concept in Russia over the Years. Human Physiology, 49(1), S96–S124. https://doi.org/10.1134/S0362119723700597

Ghorbian, M., Ghobaei-Arani, M., & Ghorbian, S. (2024). A comprehensive study on the application of machine learning in psoriasis diagnosis and treatment: Taxonomy, challenges and recommendations. Artificial Intelligence Review, 58(2), 60. https://doi.org/10.1007/s10462-024-11031-7

Hapsari, C. M. (2024). Tinjauan Hukum Terhadap Kerahasian Rekam Medis Menurut UU No. 29 Tahun 2004. Surya Medika: Jurnal Ilmiah Ilmu Keperawatan dan Ilmu Kesehatan Masyarakat, 19(2), 128–137. https://doi.org/10.32504/sm.v19i2.1030

Hermawan, D., Apriyanti, F., & Mongkau, C. D. S. (2024). Pengenalan Profesi Rekam Medis Sebagai Penyedia Informasi Kesehatan Pada SMA Negeri 1 Kusan Hilir. Jurnal Medika: Medika, 3(2), 253–258. https://doi.org/10.31004/q94qkb42

Hidayani, W. R., & Santosa, A. F. (2024). Wearable IoT dalam Bidang Kesehatan: Tantangan dan Peluang. Bincang Sains dan Teknologi, 3(02), 78–84. https://orcid.org/0000-0002-3300-7108

Juita, D. R., & Shofiyyah, N. A. (2022). Peran keluarga dalam merawat lansia. Al-Mada: Jurnal Agama, Sosial, dan Budaya, 5(2), 206–219. https://doi.org/10.31538/almada.v5i2.2413

Laila, L., Sulistyawati, S., & Hidayat, M. S. (2024). Evaluasi Penerapan Sistem Informasi Manajemen Rumah Sakit (SIMRS): Studi Literatur. Jurnal Promotif Preventif, 7(4), 710–723. https://doi.org/10.47650/jpp.v7i4.1424

Lakhmudien, L., Nugraha, E., & Setiyono, I. A. (2023). Pemahaman Perekam Medis Terhadap Penerapan Rekam Medis Elektronik Berbasis Permenkes Nomor 24 Tahun 2022. Jurnal Cakrawala Ilmiah, 2(9), 3601–3606. https://doi.org/10.53625/jcijurnalcakrawalailmiah.v2i9.5803

Majid, B., Sofi, S. A., & Jabeen, Z. (2025). Quantum machine learning: A systematic categorization based on learning paradigms, NISQ suitability, and fault tolerance. Quantum Machine Intelligence, 7(1), 39. https://doi.org/10.1007/s42484-025-00266-4

Modak, S. K. S., & Jha, V. K. (2025). Machine and deep learning techniques for the prediction of diabetics: A review. Multimedia Tools and Applications, 84(18), 19425–19549. https://doi.org/10.1007/s11042-024-19766-9

Nadiyah, S. N. A., & Prayoga, D. (2024). Transformasi Digital Sebagai Bagian dari Strategi Pemasaran Rumah Sakit: Literature Review. Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 7(2), 265–272. https://doi.org/10.56338/mppki.v7i2.4283

Nasarudin, N. A., Al Jasmi, F., Sinnott, R. O., Zaki, N., Al Ashwal, H., Mohamed, E. A., & Mohamad, M. S. (2024). A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction. Artificial Intelligence Review, 57(9), 249. https://doi.org/10.1007/s10462-024-10876-2

Nurhalizah, R. S., Ardianto, R., & Purwono, P. (2024). Analisis Supervised dan Unsupervised Learning pada Machine Learning: Systematic Literature Review. Jurnal Ilmu Komputer dan Informatika, 4(1), 61–72. https://doi.org/10.54082/jiki.168

Pandia, M. (2024). Kajian Literatur Multimedia Retrieval: Machine Learning Untuk Pengenalan Wajah. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), 7(1), 161–166. https://doi.org/10.55338/jikomsi.v7i1.2758

Pratama, F., Rahayu, E., & Andriana, S. D. (2023). Perancangan Aplikasi Rekam Medis Dengan Pendekatan Clinical Pathway Pada Pasien Rawat Jalan Dan Inap Di Puskesmas Berbasis Android. Sibatik Journal: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 2(2), 697–708. https://doi.org/10.54443/sibatik.v2i2.640

Prayogi, A., & Nasrullah, R. (2024). Artificial Intelligence dan Filsafat Ilmu: Bagaimana Filsafat Memandang Kecerdasan Buatan Sebagai Ilmu Pengetahuan. LogicLink, 144–155. https://doi.org/10.28918/logiclink.v1i2.8947

Rahbar Saadat, Y., Abbasi, A., Hejazian, S. S., Hekmatshoar, Y., Ardalan, M., Farnood, F., & Zununi Vahed, S. (2025). Combating chronic kidney disease-associated cachexia: A literature review of recent therapeutic approaches. BMC Nephrology, 26(1), 133. https://doi.org/10.1186/s12882-025-04057-8

Sulistyawati, U. S. (2024). Decoding Big Data: Mengubah Data Menjadi Keunggulan Kompetitif dalam Pengambilan Keputusan Bisnis. Jurnal Manajemen Dan Teknologi, 1(2), 58–71. https://doi.org/10.63447/jmt.v1i2.1114

Wirawan, A., & Prasetyawan, D. (2023). Analisis cluster data latar belakang ekonomi mahasiswa untuk rekomendasi penentuan uang kuliah tunggal dengan model K-Modes. INFOTECH: Jurnal Informatika & Teknologi, 4(2), 234–246. https://doi.org/10.37373/infotech.v4i2.898

Zaenudin, I., & Riyan, A. B. (2024). Perkembangan Kecerdasan Buatan (AI) Dan Dampaknya Pada Dunia Teknologi. Jurnal Informatika Utama, 2(2), 128–153. https://doi.org/10.55903/jitu.v2i2.240