K-Nearest Neighbors, decision trees and random forest for diabetes prediction
Article Sidebar
Published:
Feb 28, 2024
Keywords:
Decision Tree, Diabetes, K-Nearest Neighbor, Random Forest, Machine Learning
Section
Computer Science
Statistics Article
Article View : 73 Times
Main Article Content
Abstract
Diabetes can be treated and managed by taking medications, such as insulin injections or oral drugs, that help lower the blood sugar level. However, medications alone are not enough to control diabetes. People with diabetes also need to monitor their blood sugar level regularly, follow a balanced diet, limit the intake of sugar and carbohydrates, and avoid alcohol and tobacco. They also need to check their feet, eyes, and kidneys for any signs of damage, and seek medical attention if they notice any problems. Machine learning algorithms can help predict diabetes by learning from historical data and finding patterns that are not easily detected by human experts. They can also handle high-dimensional and noisy data, such as medical images or genomic sequences, that are relevant for diabetes diagnosis. However, machine learning algorithms also have some limitations, such as requiring a lot of data and computational resources, being prone to overfitting or underfitting, and being difficult to interpret or explain. Therefore, we conclude that random forest and decision tree are the best machine learning algorithms for predicting diabetes, and we recommend using them for future research and applications.
Downloads
Download data is not yet available.
Article Details
How to Cite
Sapriadi, S. (2024) “K-Nearest Neighbors, decision trees and random forest for diabetes prediction”, Jurnal Mantik, 7(4), pp. 3352-3360. doi: 10.35335/mantik.v7i4.4573.
References
A. Fauzi, R. Supriyadi, and N. Maulidah, (2020). Deteksi Penyakit Kanker Payudara dengan Seleksi Fitur berbasis Principal Component Analysis dan Random Fores.
Aji Primajaya., Betha Nurina Sari. (2018). Random Forest Algorithm for Prediction of Precipitation. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) Vol. 1, No.1, March 2018, pp. 27-31
Amin, M.S., Chiam, Y.K., Varathan, K.D. (2020). Identification of significant features and data mining techniques in predicting heart disease. Telemat. Inf. 2019, 36, 82–93
Barrett-Connor, E. (2020). Diabetes and heart disease. Diabetes Care 2003, 26, 2947–2958.
Callaghan, B.C., Gallagher, G., Fridman, V., Feldman, E.L. (2020). Diabetic neuropathy: What does the future hold? Diabetologia.
Dhany, Hanna Willa., Sutarman., Izhari., Fahmi. (2023). Exploratory Data Analysis (EDA) methods for healthcare classification. Journal of Intelligent Decision Support System (IDSS) 6 (4), pp. 209-215
Diba, Farah., Lydia, Maya Silvi., Sihombing, Poltak. (2023). AnalisisRandom Forest Menggunakan Principal Component Analysis Pada Data Berdimensi Tinggi. Indonesian Journal of Computer Science
Dr. S Suriya., J Joanish Muthu. (2022). Type 2 Diabetes Prediction using K-Nearest Neighbor Algorithm. Journal of Trends in Computer Science and Smart Technology (ISSN: 2582-4104)
Evita Fitri. (2023). Analisis Perbandingan Metode Regresi Linier, Random Forest Regression Dan Gradient Boosted Trees Regression Method Untuk Prediksi Harga Rumah. Journal Of Applied Computer Science And Technology (Jacost).
F. Alaa Khaleel., A. M. Al-Bakry. (2021). Diagnosis of diabetes using machine learning algorithms. Mater.
Gross, J.L., De Azevedo, M.J., Silveiro, S.P., Canani, L.H., Caramori, M.L., Zelmanovitz, T. (2018). Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care, 28, 164–176.
H. EL Massari, S., Mhammedi, Z. Sabouri., N. Gherabi. (2022). Ontology-Based Machine Learning to Predict Diabetes Patients. in Advances in Information, Communication and Cybersecurity, Cham, pp. 437–445. doi: 10.1007/978- 3-030-91738-8_40
Izhari, F., Willa Dhany, H. (2020). Comparison Of Air Quality Data Accuration Using Decision Tree And Neural Network Method. Jurnal Ipteks Terapan (Research of Applied Science and Education), 14(2), 123–127.
Putra, Purwa Hasan., Azanuddin., Purba, Bister., Dalimunthe, Yulia Agustina. (2023). Random forest and decision tree algorithms for car price prediction. Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA).
Neha Prerna Tigga, Shruti Garg. (2019). Prediction of Type 2 Diabetes using Machine Learning Classification Methods. International Conference on Computational Intelligence and Data Science (ICCIDS 2019), Volume no 167, page no.706-716.
Riki Supriyadi., Windu Gata., Nurlaelatul Maulidah., Ahmad Fauzi. (2020). Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah. Jurnal Ilmiah Ekonomi Dan Bisnis, Vol.13, No.2, Desember 2020, pp. 67 – 75.
S. Devella, Y. Yohannes, and F. N. Rahmawati. (2020). Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 310–320.
S. Srivastava. (2018). Weka: A Tool for Data preprocessing, Classification, Ensemble, Clustering and Association Rule Mining. Int. J. Comput. Appl., vol. 88, no. 10, pp. 26–29.
Saru, S. and Subashree, S., (2019). Analysis and prediction of diabetes using machine learning. International journal of emerging technology and innovative engineering, Volume no 5, Issue no 4
Sarwar, M.A., Kamal, N., Hamid, W. and Shah, M.A. (2018). Prediction of diabetes using machine learning algorithms in healthcare. In 2018 24th international conference on automation and computing (ICAC) IEEE, Page no 1-6
Wu, H., Yang, S., Huang, Z., He, J. and Wang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, pp.100-107.
Wilkinson, C., Ferris, F.L., Klein, R.E., Lee, P.P., Agardh, C.D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J.T., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110, 1677–1682.
Vergni, Lorenzo., Todisco, Francesca. (2023). A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events. Department of Agricultural, Food and Environmental Science, University of Perugia, 06124 Perugia, Italy.
Z. Sabouri, Y. Maleh, and N. Gherabi, (2022). Benchmarking Classification Algorithms for Measuring the Performance on Maintainable Applications. in Advances in Information, Communication and Cybersecurity, Cham, pp. 173–179. doi: 10.1007/978-3-030-91738-8_17
Zidian Xie, Olga Nikolayeva, MS, Jiebo Luo and Dongmei Li. (2019). Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques. National Centre for Biotechnology information, Volume no 50, Page no 100-105
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. and Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in genetics, Volume no 9, page no 115.
Aji Primajaya., Betha Nurina Sari. (2018). Random Forest Algorithm for Prediction of Precipitation. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) Vol. 1, No.1, March 2018, pp. 27-31
Amin, M.S., Chiam, Y.K., Varathan, K.D. (2020). Identification of significant features and data mining techniques in predicting heart disease. Telemat. Inf. 2019, 36, 82–93
Barrett-Connor, E. (2020). Diabetes and heart disease. Diabetes Care 2003, 26, 2947–2958.
Callaghan, B.C., Gallagher, G., Fridman, V., Feldman, E.L. (2020). Diabetic neuropathy: What does the future hold? Diabetologia.
Dhany, Hanna Willa., Sutarman., Izhari., Fahmi. (2023). Exploratory Data Analysis (EDA) methods for healthcare classification. Journal of Intelligent Decision Support System (IDSS) 6 (4), pp. 209-215
Diba, Farah., Lydia, Maya Silvi., Sihombing, Poltak. (2023). AnalisisRandom Forest Menggunakan Principal Component Analysis Pada Data Berdimensi Tinggi. Indonesian Journal of Computer Science
Dr. S Suriya., J Joanish Muthu. (2022). Type 2 Diabetes Prediction using K-Nearest Neighbor Algorithm. Journal of Trends in Computer Science and Smart Technology (ISSN: 2582-4104)
Evita Fitri. (2023). Analisis Perbandingan Metode Regresi Linier, Random Forest Regression Dan Gradient Boosted Trees Regression Method Untuk Prediksi Harga Rumah. Journal Of Applied Computer Science And Technology (Jacost).
F. Alaa Khaleel., A. M. Al-Bakry. (2021). Diagnosis of diabetes using machine learning algorithms. Mater.
Gross, J.L., De Azevedo, M.J., Silveiro, S.P., Canani, L.H., Caramori, M.L., Zelmanovitz, T. (2018). Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care, 28, 164–176.
H. EL Massari, S., Mhammedi, Z. Sabouri., N. Gherabi. (2022). Ontology-Based Machine Learning to Predict Diabetes Patients. in Advances in Information, Communication and Cybersecurity, Cham, pp. 437–445. doi: 10.1007/978- 3-030-91738-8_40
Izhari, F., Willa Dhany, H. (2020). Comparison Of Air Quality Data Accuration Using Decision Tree And Neural Network Method. Jurnal Ipteks Terapan (Research of Applied Science and Education), 14(2), 123–127.
Putra, Purwa Hasan., Azanuddin., Purba, Bister., Dalimunthe, Yulia Agustina. (2023). Random forest and decision tree algorithms for car price prediction. Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA).
Neha Prerna Tigga, Shruti Garg. (2019). Prediction of Type 2 Diabetes using Machine Learning Classification Methods. International Conference on Computational Intelligence and Data Science (ICCIDS 2019), Volume no 167, page no.706-716.
Riki Supriyadi., Windu Gata., Nurlaelatul Maulidah., Ahmad Fauzi. (2020). Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah. Jurnal Ilmiah Ekonomi Dan Bisnis, Vol.13, No.2, Desember 2020, pp. 67 – 75.
S. Devella, Y. Yohannes, and F. N. Rahmawati. (2020). Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 310–320.
S. Srivastava. (2018). Weka: A Tool for Data preprocessing, Classification, Ensemble, Clustering and Association Rule Mining. Int. J. Comput. Appl., vol. 88, no. 10, pp. 26–29.
Saru, S. and Subashree, S., (2019). Analysis and prediction of diabetes using machine learning. International journal of emerging technology and innovative engineering, Volume no 5, Issue no 4
Sarwar, M.A., Kamal, N., Hamid, W. and Shah, M.A. (2018). Prediction of diabetes using machine learning algorithms in healthcare. In 2018 24th international conference on automation and computing (ICAC) IEEE, Page no 1-6
Wu, H., Yang, S., Huang, Z., He, J. and Wang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, pp.100-107.
Wilkinson, C., Ferris, F.L., Klein, R.E., Lee, P.P., Agardh, C.D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J.T., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110, 1677–1682.
Vergni, Lorenzo., Todisco, Francesca. (2023). A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events. Department of Agricultural, Food and Environmental Science, University of Perugia, 06124 Perugia, Italy.
Z. Sabouri, Y. Maleh, and N. Gherabi, (2022). Benchmarking Classification Algorithms for Measuring the Performance on Maintainable Applications. in Advances in Information, Communication and Cybersecurity, Cham, pp. 173–179. doi: 10.1007/978-3-030-91738-8_17
Zidian Xie, Olga Nikolayeva, MS, Jiebo Luo and Dongmei Li. (2019). Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques. National Centre for Biotechnology information, Volume no 50, Page no 100-105
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. and Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in genetics, Volume no 9, page no 115.
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.