Model Prediksi Penyakit Ginjal Menggunakan Algoritma Neural Network
DOI:
https://doi.org/10.55606/juisik.v5i2.1219Keywords:
Medical-data, machine-learning, predictive-models, neural-networks, kidney-diseaseAbstract
Neural networks are the focal point of machine learning research. Their applications span across various fields and solve complex and intricate problems. Neural networks have now been applied in health image processing to detect various diseases such as cancer and diabetes. Another disease that threatens our health is kidney disease. This disease is becoming more common due to the substances and elements we consume. Death is imminent and inevitable within a few days without at least one functioning kidney. Ignoring kidney damage can lead to chronic kidney disease leading to death. Often, Kidney Disease and its symptoms are mild and gradual, often going unnoticed for years and only being realized recently. This study used 400 patients with 10 attributes as our dataset from Bade General Hospital. We used a Neural network model to predict the presence or absence of kidney disease susceptibility in patients. The model yielded an accuracy of 98%. Furthermore, we identified and highlighted the important features to rank the features used in kidney disease prediction. The results revealed that two attributes; Creatinine and Bicarbonate had the highest influence on kidney disease susceptibility prediction.
References
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Jahan, N., Rahman, M. M., & Islam, M. R. (2021). Application of Machine Learning Algorithms in Healthcare Sector for Disease Prediction. Journal of Biomedical Informatics, 118, 103799.
Jain, D., & Singh, V. (2018). Feature selection and classification systems for chronic disease prediction: A review. Egyptian Informatics Journal, 19(3), 179-189.
Joshi, T. N., & Chawan, P. M. (2018). Logistic Regression and SVM Based Diabetes Prediction System. International Journal For Technological Research In Engineering, 11(5), 2347-4718, July 2018
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Kriplani, H., Patel, B., & Roy, S. (2019). Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique. In Computer Aided Intervention and Diagnostics in Clinical and Medical Images (pp. 179-187). Springer, Cham. Chicago.
Kumar, S. (2018). Chronic Kidney Disease Prediction Using Machine Learning. International Journal of Computer Science and Information Security (IJCSIS), 16(4).
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National Kidney Foundation (NKF). (2015). Global Facts: About Kidney Disease. In National Kidney Foundation.
Saxena, S., & Sharma, S. (2020). Artificial Neural Network in Medical Diagnosis: A Review. International Journal of Computer Applications, 176(29), 1–5.
Scholar, P. G. (2018). Chronic Kidney Disease Prediction Using Machine Learning. International Journal of Computer Science and Information Security (IJCSIS), 16(4).
Shafi, N., Bukhari, F., Iqbal, W., Almustafa, K. M., Asif, M., & Nawaz, Z. (2020). Cleft prediction before birth using deep neural network. Health Informatics Journal,1(18) 1460458220911789.
Sharma, S., & Parmar, M. (2020). Heart Diseases Prediction using Deep Learning Neural Network Model. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(3).
WHO. (2006). World Health Organization: Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia. Geneva, World Health Org. WHO2.
World Kidney Day. (2017). Chronic Kidney Disease - World Kidney Day. ISN – Global Operations Center
Ahmed, R. M., &Alshebly, O. Q. (2019). Prediction and Factors Affecting of Chronic Kidney Disease Diagnosis using Artificial Neural Networks Model and Logistic Regression Model. Iraqi Journal of Statistical Sciences, 16(28), 140-159.
Arafat, F., Fatema, K., & Islam, S. (2018). Classification of chronic kidney disease (ckd) using data mining techniques (Doctoral dissertation, Daffodil International University).
Arbain, A. N., & Balakrishnan, B. Y. P. (2019). A Comparison of Data Mining Algorithms for Liver Disease Prediction on Imbalanced Data. International Journal of Data Science and Advanced Analytics, 1(1), 1-11.
Ayon, S. I., & Islam, M. (2019). Diabetes Prediction: A Deep Learning Approach. International Journal of Information Engineering & Electronic Business, 11(2).
Başar, M. D., & Akan, A. (2018). Chronic Kidney Disease Prediction with Reduced Individual Classifiers. Journal of Electrical and Electronics Engineering, 18(2), 249-255.
Chahal, A., & Gulia, P. (2019). Machine learning and deep learning. International Journal of Innovative Technology and Exploring Engineering, 8(2), 2278-3075.
Ge, Y., Wang, Q., Wang, L., Wu, H., Peng, C., Wang, J. & Yi, Y. (2019). Predicting post-stroke pneumonia using deep neural network approaches. International Journal of Medical Informatics, 132, 103986.
Geri, G., Stengel, B., Jacquelinet, C., Aegerter, P., Massy, Z. A., &Vieillard-Baron, A. (2018). Prediction of chronic kidney disease after acute kidney injury in ICU patients: study protocol for the PREDICT multicenter prospective observational study. Annals of Intensive Care, 8(1), 77.
Jahan, N., Rahman, M. M., & Islam, M. R. (2021). Application of Machine Learning Algorithms in Healthcare Sector for Disease Prediction. Journal of Biomedical Informatics, 118, 103799.
Jain, D., & Singh, V. (2018). Feature selection and classification systems for chronic disease prediction: A review. Egyptian Informatics Journal, 19(3), 179-189.
Joshi, T. N., & Chawan, P. M. (2018). Logistic Regression and SVM Based Diabetes Prediction System. International Journal For Technological Research In Engineering, 11(5), 2347-4718, July 2018
Khurana, M., Sharma, N., & Singh, V. (2019). Prediction of Chronic Kidney Disease Using Machine Learning Algorithms. Procedia Computer Science, 167, 674–683.
Kriplani, H., Patel, B., & Roy, S. (2019). Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique. In Computer Aided Intervention and Diagnostics in Clinical and Medical Images (pp. 179-187). Springer, Cham. Chicago.
Kumar, S. (2018). Chronic Kidney Disease Prediction Using Machine Learning. International Journal of Computer Science and Information Security (IJCSIS), 16(4).
Luyckx, V. A., Tonelli, M., & Stanifer, J. W. (2018). The global burden of kidney disease and the sustainable development goals. Bulletin of the World Health Organization. , 96(6), 414.
National Kidney Foundation (NKF). (2015). Global Facts: About Kidney Disease. In National Kidney Foundation.
Saxena, S., & Sharma, S. (2020). Artificial Neural Network in Medical Diagnosis: A Review. International Journal of Computer Applications, 176(29), 1–5.
Scholar, P. G. (2018). Chronic Kidney Disease Prediction Using Machine Learning. International Journal of Computer Science and Information Security (IJCSIS), 16(4).
Shafi, N., Bukhari, F., Iqbal, W., Almustafa, K. M., Asif, M., & Nawaz, Z. (2020). Cleft prediction before birth using deep neural network. Health Informatics Journal,1(18) 1460458220911789.
Sharma, S., & Parmar, M. (2020). Heart Diseases Prediction using Deep Learning Neural Network Model. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(3).
WHO. (2006). World Health Organization: Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia. Geneva, World Health Org. WHO2.
World Kidney Day. (2017). Chronic Kidney Disease - World Kidney Day. ISN – Global Operations Center
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