Effect of dimension reduction with PCA and machine learning algorithms on diabetes diagnosis performance

Yıl 2024, Cilt: 8 Sayı: 3, 447 – 456, 28.07.2024

Yavuz Bahadir Koca , Elif Aktepe

https://doi.org/10.31127/tuje.1413087

Öz

Diabetes, a long-term metabolic disorder, causes persistently high blood sugar and presents a significant global health challenge. Early diagnosis is of vital importance in mitigating the effects of diabetes. This study aims to investigate diabetes diagnosis and risk prediction using a comprehensive diabetes dataset created in 2023. The dataset contains clinical and anthropometric data of patients. Data simplification was successfully applied to clean unnecessary information and reduce data dimensionality. Additionally, methods like Principal Component Analysis were applied to decrease the number of variables in the dataset. These analyses rendered the dataset more manageable and improved its performance. In this study, a dataset encompassing health data of a total of 100,000 individuals was utilized. This dataset consists of 8 input features and 1 output feature. The primary objective is to determine the algorithm that exhibits the best performance for diabetes diagnosis. There was no missing data during the data preprocessing stage, and the necessary transformations were carried out successfully. Nine different machine learning algorithms were applied to the dataset in this study. Each algorithm employed various modelling approaches to evaluate its performance in diagnosing diabetes. The results demonstrate that machine learning models are successful in predicting the presence of diabetes and the risk of developing it in healthy individuals. Particularly, the random forest model provided superior results across all performance metrics. This study provides significant findings that can shed light on future research in diabetes diagnosis and risk prediction. Dimensionality reduction techniques have proven to be valuable in data analysis and have highlighted the potential to facilitate diabetes diagnosis, thereby enhancing the quality of life for patients.

Anahtar Kelimeler

Diabetes, Data analysis, Machine learning, PCA, Random forest

Kaynakça

• Sowah, R. A., Bampoe-Addo, A. A., Armoo, S. K., Saalia, F. K., Gatsi, F., & Sarkodie-Mensah, B. (2020). Design and development of diabetes management system using machine learning. International Journal of Telemedicine and Applications, 2020(1), 8870141. https://doi.org/10.1155/2020/8870141
• Yahyaoui, A., Jamil, A., Rasheed, J., & Yesiltepe, M. (2019, November). A decision support system for diabetes prediction using machine learning and deep learning techniques. In 2019 1st International Informatics and Software Engineering Conference (UBMYK), 1-4. https://doi.org/10.1109/UBMYK48245.2019.8965556
• Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104-116. https://doi.org/10.1016/j.csbj.2016.12.005
• Abudnejad, N., Salehpour, M., & Saadati, Z. (2023). Theoretical evaluation of boron carbide nanotubes as non-enzymatic glucose sensors. Chemical Physics Letters, 823, 140510. https://doi.org/10.1016/j.cplett.2023.140510
• Başer, B. Ö., Yangın, M., & Sarıdaş, E. S. (2021). Makine öğrenmesi teknikleriyle diyabet hastalığının sınıflandırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 112-120. https://doi.org/10.19113/sdufenbed.842460
• World Health Organization WHO European Regional Obesity Report (2022). World Health Organization. Regional Office for Europe. ISBN 9289057734.
• Sun, J., Ren, J., Hu, X., Hou, Y., & Yang, Y. (2021). Therapeutic effects of Chinese herbal medicines and their extracts on diabetes. Biomedicine & Pharmacotherapy, 142, 111977. https://doi.org/10.1016/j.biopha.2021.111977
• Hasanzad, M., Aghaei Meybodi, H. R., Sarhangi, N., & Larijani, B. (2022). Artificial intelligence perspective in the future of endocrine diseases. Journal of Diabetes & Metabolic Disorders, 21(1), 971-978. https://doi.org/10.1007/s40200-021-00949-2
• Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104-116. https://doi.org/10.1016/j.csbj.2016.12.005
• Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2017). Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics, 6(3), 65. https://doi.org/10.3390/electronics6030065
• Zherebtsov, E. A., Zharkikh, E. V., Kozlov, I. O., Loktionova, Y. I., Zherebtsova, A. I., Rafailov, I. E., … & Rafailov, E. U. (2019, June). Wearable sensor system for multipoint measurements of blood perfusion: pilot studies in patients with diabetes mellitus. In European Conference on Biomedical Optics, 11079_62. https://doi.org/10.1117/12.2526966
• Mujumdar, A., & Vaidehi, V. (2019). Diabetes prediction using machine learning algorithms. Procedia Computer Science, 165, 292-299. https://doi.org/10.1016/j.procs.2020.01.047
• Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G. (2020). Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Scientific Reports, 10(1), 11981. https://doi.org/10.1038/s41598-020-68771-z
• Nadesh, R. K., & Arivuselvan, K. (2020). Type 2: diabetes mellitus prediction using deep neural networks classifier. International Journal of Cognitive Computing in Engineering, 1, 55-61. https://doi.org/10.1016/j.ijcce.2020.10.002
• Lai, H., Huang, H., Keshavjee, K., Guergachi, A., & Gao, X. (2019). Predictive models for diabetes mellitus using machine learning techniques. BMC Endocrine Disorders, 19, 1-9. https://doi.org/10.1186/s12902-019-0436-6
• Soni, M., & Varma, S. (2020). Diabetes prediction using machine learning techniques. International Journal of Engineering Research & Technology (IJERT), 9(09), 921-925.
• Tasin, I., Nabil, T. U., Islam, S., & Khan, R. (2023). Diabetes prediction using machine learning and explainable AI techniques. Healthcare Technology Letters, 10(1-2), 1-10. https://doi.org/10.1049/htl2.12039
• Cahn, A., Shoshan, A., Sagiv, T., Yesharim, R., Goshen, R., Shalev, V., & Raz, I. (2020). Prediction of progression from pre‐diabetes to diabetes: development and validation of a machine learning model. Diabetes/metabolism Research and Reviews, 36(2), e3252. https://doi.org/10.1002/dmrr.3252
• Dinh, A., Miertschin, S., Young, A., & Mohanty, S. D. (2019). A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Medical Informatics and Decision Making, 19(1), 1-15. https://doi.org/10.1186/s12911-019-0918-5
• Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531. https://doi.org/10.1109/ACCESS.2020.2989857
• Kaur, H., & Kumari, V. (2022). Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics, 18(1/2), 90-100. https://doi.org/10.1016/j.aci.2018.12.004
• Birjais, R., Mourya, A. K., Chauhan, R., & Kaur, H. (2019). Prediction and diagnosis of future diabetes risk: a machine learning approach. SN Applied Sciences, 1, 1-8. https://doi.org/10.1007/s42452-019-1117-9
• Nandy, S. (2023). Kaggle. https://www.kaggle.com/datasets/sharmisthanandy/diabetes
• Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117-128. https://doi.org/10.1016/j.measurement.2018.01.022
• Schober, P., & Vetter, T. R. (2021). Statistical Minute Logistic Regression in Medical Research.
• Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(2), 140-147. https://doi.org/10.38094/jastt1457
• Choudhury, A., & Gupta, D. (2019). A survey on medical diagnosis of diabetes using machine learning techniques. In Recent Developments in Machine Learning and Data Analytics: IC3 2018, 740, 67-78. https://doi.org/10.1007/978-981-13-1280-9_6
• Vijayan, V. V., & Anjali, C. (2015, December). Prediction and diagnosis of diabetes mellitus—A machine learning approach. In 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 122-127. https://doi.org/10.1109/RAICS.2015.7488400
• Ghosh, P., Azam, S., Karim, A., Hassan, M., Roy, K., & Jonkman, M. (2021). A comparative study of different machine learning tools in detecting diabetes. Procedia Computer Science, 192, 467-477. https://doi.org/10.1016/j.procs.2021.08.048
• Al-Askar, H., Radi, N., & MacDermott, Á. (2016). Recurrent neural networks in medical data analysis and classifications. In Applied Computing in Medicine and Health, 147-165. https://doi.org/10.1016/B978-0-12-803468-2.00007-2
• Maćkiewicz, A., & Ratajczak, W. (1993). Principal components analysis (PCA). Computers & Geosciences, 19(3), 303-342. https://doi.org/10.1016/0098-3004(93)90090-R

Yıl 2024, Cilt: 8 Sayı: 3, 447 – 456, 28.07.2024

Yavuz Bahadir Koca , Elif Aktepe

https://doi.org/10.31127/tuje.1413087

Öz

Kaynakça

• Sowah, R. A., Bampoe-Addo, A. A., Armoo, S. K., Saalia, F. K., Gatsi, F., & Sarkodie-Mensah, B. (2020). Design and development of diabetes management system using machine learning. International Journal of Telemedicine and Applications, 2020(1), 8870141. https://doi.org/10.1155/2020/8870141
• Yahyaoui, A., Jamil, A., Rasheed, J., & Yesiltepe, M. (2019, November). A decision support system for diabetes prediction using machine learning and deep learning techniques. In 2019 1st International Informatics and Software Engineering Conference (UBMYK), 1-4. https://doi.org/10.1109/UBMYK48245.2019.8965556
• Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104-116. https://doi.org/10.1016/j.csbj.2016.12.005
• Abudnejad, N., Salehpour, M., & Saadati, Z. (2023). Theoretical evaluation of boron carbide nanotubes as non-enzymatic glucose sensors. Chemical Physics Letters, 823, 140510. https://doi.org/10.1016/j.cplett.2023.140510
• Başer, B. Ö., Yangın, M., & Sarıdaş, E. S. (2021). Makine öğrenmesi teknikleriyle diyabet hastalığının sınıflandırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 112-120. https://doi.org/10.19113/sdufenbed.842460
• World Health Organization WHO European Regional Obesity Report (2022). World Health Organization. Regional Office for Europe. ISBN 9289057734.
• Sun, J., Ren, J., Hu, X., Hou, Y., & Yang, Y. (2021). Therapeutic effects of Chinese herbal medicines and their extracts on diabetes. Biomedicine & Pharmacotherapy, 142, 111977. https://doi.org/10.1016/j.biopha.2021.111977
• Hasanzad, M., Aghaei Meybodi, H. R., Sarhangi, N., & Larijani, B. (2022). Artificial intelligence perspective in the future of endocrine diseases. Journal of Diabetes & Metabolic Disorders, 21(1), 971-978. https://doi.org/10.1007/s40200-021-00949-2
• Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104-116. https://doi.org/10.1016/j.csbj.2016.12.005
• Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2017). Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics, 6(3), 65. https://doi.org/10.3390/electronics6030065
• Zherebtsov, E. A., Zharkikh, E. V., Kozlov, I. O., Loktionova, Y. I., Zherebtsova, A. I., Rafailov, I. E., … & Rafailov, E. U. (2019, June). Wearable sensor system for multipoint measurements of blood perfusion: pilot studies in patients with diabetes mellitus. In European Conference on Biomedical Optics, 11079_62. https://doi.org/10.1117/12.2526966
• Mujumdar, A., & Vaidehi, V. (2019). Diabetes prediction using machine learning algorithms. Procedia Computer Science, 165, 292-299. https://doi.org/10.1016/j.procs.2020.01.047
• Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G. (2020). Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Scientific Reports, 10(1), 11981. https://doi.org/10.1038/s41598-020-68771-z
• Nadesh, R. K., & Arivuselvan, K. (2020). Type 2: diabetes mellitus prediction using deep neural networks classifier. International Journal of Cognitive Computing in Engineering, 1, 55-61. https://doi.org/10.1016/j.ijcce.2020.10.002
• Lai, H., Huang, H., Keshavjee, K., Guergachi, A., & Gao, X. (2019). Predictive models for diabetes mellitus using machine learning techniques. BMC Endocrine Disorders, 19, 1-9. https://doi.org/10.1186/s12902-019-0436-6
• Soni, M., & Varma, S. (2020). Diabetes prediction using machine learning techniques. International Journal of Engineering Research & Technology (IJERT), 9(09), 921-925.
• Tasin, I., Nabil, T. U., Islam, S., & Khan, R. (2023). Diabetes prediction using machine learning and explainable AI techniques. Healthcare Technology Letters, 10(1-2), 1-10. https://doi.org/10.1049/htl2.12039
• Cahn, A., Shoshan, A., Sagiv, T., Yesharim, R., Goshen, R., Shalev, V., & Raz, I. (2020). Prediction of progression from pre‐diabetes to diabetes: development and validation of a machine learning model. Diabetes/metabolism Research and Reviews, 36(2), e3252. https://doi.org/10.1002/dmrr.3252
• Dinh, A., Miertschin, S., Young, A., & Mohanty, S. D. (2019). A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Medical Informatics and Decision Making, 19(1), 1-15. https://doi.org/10.1186/s12911-019-0918-5
• Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531. https://doi.org/10.1109/ACCESS.2020.2989857
• Kaur, H., & Kumari, V. (2022). Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics, 18(1/2), 90-100. https://doi.org/10.1016/j.aci.2018.12.004
• Birjais, R., Mourya, A. K., Chauhan, R., & Kaur, H. (2019). Prediction and diagnosis of future diabetes risk: a machine learning approach. SN Applied Sciences, 1, 1-8. https://doi.org/10.1007/s42452-019-1117-9
• Nandy, S. (2023). Kaggle. https://www.kaggle.com/datasets/sharmisthanandy/diabetes
• Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117-128. https://doi.org/10.1016/j.measurement.2018.01.022
• Schober, P., & Vetter, T. R. (2021). Statistical Minute Logistic Regression in Medical Research.
• Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(2), 140-147. https://doi.org/10.38094/jastt1457
• Choudhury, A., & Gupta, D. (2019). A survey on medical diagnosis of diabetes using machine learning techniques. In Recent Developments in Machine Learning and Data Analytics: IC3 2018, 740, 67-78. https://doi.org/10.1007/978-981-13-1280-9_6
• Vijayan, V. V., & Anjali, C. (2015, December). Prediction and diagnosis of diabetes mellitus—A machine learning approach. In 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 122-127. https://doi.org/10.1109/RAICS.2015.7488400
• Ghosh, P., Azam, S., Karim, A., Hassan, M., Roy, K., & Jonkman, M. (2021). A comparative study of different machine learning tools in detecting diabetes. Procedia Computer Science, 192, 467-477. https://doi.org/10.1016/j.procs.2021.08.048
• Al-Askar, H., Radi, N., & MacDermott, Á. (2016). Recurrent neural networks in medical data analysis and classifications. In Applied Computing in Medicine and Health, 147-165. https://doi.org/10.1016/B978-0-12-803468-2.00007-2
• Maćkiewicz, A., & Ratajczak, W. (1993). Principal components analysis (PCA). Computers & Geosciences, 19(3), 303-342. https://doi.org/10.1016/0098-3004(93)90090-R

Toplam 31 adet kaynakça vardır.

Ayrıntılar

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