Deteksi Nefropati Diabetik Pada Pasien Diabetes Melitus Menggunakan Regresi Logistik
Kata Kunci:
diabetes melitus, nefropati diabetik, log transformasi, oversampling, gradient descent, regresi logistikAbstrak
Diabetes Melitus adalah penyakit metabolik kronis yang ditandai oleh hiperglikemia akibat gangguan produksi atau penggunaan insulin. Diabetes melitus dapat menyebabkan komplikasi serius, salah satunya Nefropati Diabetik, yang merupakan kerusakan pada ginjal akibat diabetes. Berdasarkan data International Diabetes Federation (IDF), jumlah penderita diabetes meningkat signifikan setiap tahun. Penelitian ini bertujuan mendeteksi kemungkinan terjadinya Nefropati Diabetik pada pasien Diabetes melitus menggunakan model regresi logistik. Tahap awal penelitian ini melibatkan pengolahan data (preprocessing) untuk mengatasi masalah data outlier yang berlebih dan ketidakseimbangan kelas. Metode balancing data dengan oversampling dan log transformasi digunakan untuk meningkatkan kualitas dataset. Proses pelatihan model dilakukan dengan algoritma gradient descent untuk mengoptimalkan parameter regresi logistik. Evaluasi model menunjukkan hasil yang sangat baik, dengan akurasi, presisi, recall, dan F1-score rata-rata mencapai 0,98. Penelitian ini diharapkan dapat menjadi alat prediksi yang efektif dalam mendeteksi Nefropati Diabetik pada pasien diabetes melitus, sehingga membantu pencegahan komplikasi yang lebih serius. Implementasi model ini dapat berkontribusi pada peningkatan kualitas pelayanan kesehatan, terutama dalam penanganan kasus diabetes melitus.
Referensi
Amelia, R., Sari, D. K., Muzasti, R. A., & Wijaya, H. (2022). Correlation of Cystatin-c with Albumin Creatinine Ratio for the Diagnosis of Diabetic Nephropathy in Patients with Type 2 Diabetes: A Cross-sectional Study in Medan Indonesia. Open Access Macedonian Journal of Medical Sciences, 10(T7), 12–15. https://doi.org/10.3889/oamjms.2022.9249
Boateng, E. Y., & Abaye, D. A. (2019). A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing, 07(04), 190–207. https://doi.org/10.4236/jdaip.2019.74012
Galicia-Garcia, U., Benito-Vicente, A., Jebari, S., Larrea-Sebal, A., Siddiqi, H., Uribe, K. B., Ostolaza, H., & Martín, C. (2020). Pathophysiology of type 2 diabetes mellitus. In International Journal of Molecular Sciences (Vol. 21, Issue 17, pp. 1–34). MDPI AG. https://doi.org/10.3390/ijms21176275
Ganie, A. G., & Dadvandipour, S. (2023). From big data to smart data: a sample gradient descent approach for machine learning. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00839-9
Joseph, C., Angelo, J. R., Laskin, B. L., & Hingorani, S. (2020). 11 - Hematopoietic cell transplant associated kidney injury. In K. W. Finkel, M. A. Perazella, & E. P. Cohen (Eds.), Onco-Nephrology (pp. 89-98.e3). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-323-54945-5.00020-5
Mohammed, R., Rawashdeh, J., & Abdullah, M. (2020). Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results. 2020 11th International Conference on Information and Communication Systems, ICICS 2020, 243–248. https://doi.org/10.1109/ICICS49469.2020.239556
Natesan, V., & Kim, S. J. (2021). Diabetic nephropathy – A review of risk factors, progression, mechanism, and dietary management. In Biomolecules and Therapeutics (Vol. 29, Issue 4, pp. 365–372). Korean Society of Applied Pharmacology. https://doi.org/10.4062/biomolther.2020.204
Roosyidah, N. A. N., & Supriyatna, P. K. (2022). Pemodelan Regresi Logistik untuk Diagnosis Dini Infeksi Covid-19 di Indonesia. Jambura Journal of Mathematics, 4(2), 232–246. https://doi.org/10.34312/jjom.v4i2.12653
Rustam, Z., Zhafarina, F., Saragih, G. S., & Hartini, S. (2021). Pancreatic cancer classification using logistic regression and random forest. IAES International Journal of Artificial Intelligence, 10(2), 476–481. https://doi.org/10.11591/IJAI.V10.I2.PP476-481
Sabath, E. (2023). 18 - Arsenic, kidney, and urinary bladder disorders. In S. J. S. Flora (Ed.), Handbook of Arsenic Toxicology (Second Edition) (Second Edition, pp. 485–500). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-323-89847-8.00011-0
Sulaiman, M. K. (2019). Diabetic nephropathy: Recent advances in pathophysiology and challenges in dietary management. In Diabetology and Metabolic Syndrome (Vol. 11, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s13098-019-0403-4
Van Nga, V., Thi Anh Kim, L., Thi Quynh, D., Thi My Dung, D., Thi Binh Minh, N., Thi Diem Hong, L., & Thi Thom, V. (2021). Applying Logistic Regression to Predict Diabetic Nephropathy Based on Some Clinical and Paraclinical Characteristics of Type 2 Diabetic Patients. VNU Journal of Science: Medical and Pharmaceutical Sciences, 37(2), 77–85. https://doi.org/10.25073/2588-1132/vnumps.4312
West, R. M. (2022). Best practice in statistics: The use of log transformation. Annals of Clinical Biochemistry, 59(3), 162–165. https://doi.org/10.1177/00045632211050531
Yun, H. (2021). Prediction model of algal blooms using logistic regression and confusion matrix. International Journal of Electrical and Computer Engineering, 11(3), 2407–2413. https://doi.org/10.11591/ijece.v11i3.pp2407-2413
Amelia, R., Sari, D. K., Muzasti, R. A., & Wijaya, H. (2022). Correlation of Cystatin-c with Albumin Creatinine Ratio for the Diagnosis of Diabetic Nephropathy in Patients with Type 2 Diabetes: A Cross-sectional Study in Medan Indonesia. Open Access Macedonian Journal of Medical Sciences, 10(T7), 12–15. https://doi.org/10.3889/oamjms.2022.9249
Boateng, E. Y., & Abaye, D. A. (2019). A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing, 07(04), 190–207. https://doi.org/10.4236/jdaip.2019.74012
Galicia-Garcia, U., Benito-Vicente, A., Jebari, S., Larrea-Sebal, A., Siddiqi, H., Uribe, K. B., Ostolaza, H., & Martín, C. (2020). Pathophysiology of type 2 diabetes mellitus. In International Journal of Molecular Sciences (Vol. 21, Issue 17, pp. 1–34). MDPI AG. https://doi.org/10.3390/ijms21176275
Ganie, A. G., & Dadvandipour, S. (2023). From big data to smart data: a sample gradient descent approach for machine learning. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00839-9
Joseph, C., Angelo, J. R., Laskin, B. L., & Hingorani, S. (2020). 11 - Hematopoietic cell transplant associated kidney injury. In K. W. Finkel, M. A. Perazella, & E. P. Cohen (Eds.), Onco-Nephrology (pp. 89-98.e3). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-323-54945-5.00020-5
Mohammed, R., Rawashdeh, J., & Abdullah, M. (2020). Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results. 2020 11th International Conference on Information and Communication Systems, ICICS 2020, 243–248. https://doi.org/10.1109/ICICS49469.2020.239556
Natesan, V., & Kim, S. J. (2021). Diabetic nephropathy – A review of risk factors, progression, mechanism, and dietary management. In Biomolecules and Therapeutics (Vol. 29, Issue 4, pp. 365–372). Korean Society of Applied Pharmacology. https://doi.org/10.4062/biomolther.2020.204
Roosyidah, N. A. N., & Supriyatna, P. K. (2022). Pemodelan Regresi Logistik untuk Diagnosis Dini Infeksi Covid-19 di Indonesia. Jambura Journal of Mathematics, 4(2), 232–246. https://doi.org/10.34312/jjom.v4i2.12653
Rustam, Z., Zhafarina, F., Saragih, G. S., & Hartini, S. (2021). Pancreatic cancer classification using logistic regression and random forest. IAES International Journal of Artificial Intelligence, 10(2), 476–481. https://doi.org/10.11591/IJAI.V10.I2.PP476-481
Sabath, E. (2023). 18 - Arsenic, kidney, and urinary bladder disorders. In S. J. S. Flora (Ed.), Handbook of Arsenic Toxicology (Second Edition) (Second Edition, pp. 485–500). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-323-89847-8.00011-0
Sulaiman, M. K. (2019). Diabetic nephropathy: Recent advances in pathophysiology and challenges in dietary management. In Diabetology and Metabolic Syndrome (Vol. 11, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s13098-019-0403-4
Van Nga, V., Thi Anh Kim, L., Thi Quynh, D., Thi My Dung, D., Thi Binh Minh, N., Thi Diem Hong, L., & Thi Thom, V. (2021). Applying Logistic Regression to Predict Diabetic Nephropathy Based on Some Clinical and Paraclinical Characteristics of Type 2 Diabetic Patients. VNU Journal of Science: Medical and Pharmaceutical Sciences, 37(2), 77–85. https://doi.org/10.25073/2588-1132/vnumps.4312
West, R. M. (2022). Best practice in statistics: The use of log transformation. Annals of Clinical Biochemistry, 59(3), 162–165. https://doi.org/10.1177/00045632211050531
Yun, H. (2021). Prediction model of algal blooms using logistic regression and confusion matrix. International Journal of Electrical and Computer Engineering, 11(3), 2407–2413. https://doi.org/10.11591/ijece.v11i3.pp2407-2413
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