Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, , 36 - 41, 31.03.2023
https://doi.org/10.22399/ijcesen.1185474

Öz

Kaynakça

  • [1]Diabetes Overview, (2022). https://www.who.int/ne ws-room/fact-sheets/detail/diabetes
  • [2]Diabetes, (2022). https://www.who.int/health-topics /diabetes#tab=tab_1
  • [3]Güldoğan, E., Zeynep, T. U. N. Ç., Ayça, A. C. E. T., & ÇOLAK, C. (2020). Performance evaluation of different artificial neural network models in the classification of type 2 diabetes mellitus. The Journal of Cognitive Systems, 5(1), 23-32.
  • [4]Maulidah, N., Abdilah, A., Nurlelah, E., Gata, W., & Hasan, F. N. (2020). Seleksi Fitur Klasifikasi Penyakit Diabetes Menggunakan Particle Swarm Optimization (PSO) Pada Algoritma Naive Bayes. Elkom: Jurnal Elektronika dan Komputer, 13(2), 40-48.
  • [5]Tigga, N. P., & Garg, S. (2020). Prediction of type 2 diabetes using machine learning classification methods. Procedia Computer Science, 167, 706-716.
  • [6]Jakka, A., & Vakula Rani, J. (2019). Performance evaluation of machine learning models for diabetes prediction. Int. J. Innov. Technol. Explor. Eng.(IJITEE), 8(11).
  • [7]Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia computer science, 132, 1578-1585.
  • [8]Feng, T. C., Li, T. H. S., & Kuo, P. H. (2015). Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming. Applied Mathematical Modelling, 39(23-24), 7401-7419.
  • [9]Pima Indians Diabetes Database, (2022). https://data. world/data-society/pima-indians-diabetes-database
  • [10]Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • [11]Yakut Ö., Bolat E. D. (2020). Arrhythmia Diagnosis from ECG Signal Using Tree-based Machine Learning Methods. International Journal of Mathematic Engineering and Natural Sciences, 4(16),954-964.
  • [12]Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1), 3-42.
  • [13]MacKay, D. J. (1998). Introduction to Gaussian processes. NATO ASI series F computer and systems sciences, 168, 133-166.
  • [14]Yakut, Ö. (2020, November 19-21). Cloud Computing Based Voting Classifier Method Used For Survival Prediction of Heart Failure Patients. International Conference on Engineering Technologies, Konya-Turkey. https://icente.selcuk. edu.tr/
  • [15]Yakut, Ö. (2020, November 28-29). Comparison of Clustering Methods For Early Stage Diabetes Risk Prediction Using Cloud Computing. International Black Sea Coastline Countries Symposium 5, Zonguldak-Turkey.
  • [16]Google Colab Notebbook, (2022). https://colab. research. google.com
  • [17]Yakut, Ö., Bolat, E. D. (2020). An Efficient Arrhythmic Heartbeat Classification Method Using ECG Morphology Based Features. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 7(13), 200-212.
  • [18]Yakut, Ö., Timuş, O., Bolat, E. D. (2016). HRV Analysis Based Arrhythmic Beat Detection Us-ing kNN Classifier. International Journal of Biomedical and Biological Engineering, 10(2), 60-63.

Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

Yıl 2023, , 36 - 41, 31.03.2023
https://doi.org/10.22399/ijcesen.1185474

Öz

Diabetes is getting more and more common around the world. People suffer from diabetes or live at risk associated with this disease. It is necessary to prevent health problems caused by diabetes, to reduce the risk of diabetes and to reduce a load of diabetes on the health system. Therefore, it is important to diagnose and treat diabetic patients early. In this study, Pima Indian Diabetes (PID) database was used to predict diabetes. Random Forest Classifier, Extra Tree Classifier and Gaussian Process Classifier machine learning methods have been used to predict whether individuals have diabetes or not. In this study, the method with the highest prediction accuracy was determined as the Random Forest Classifier. The accuracy of the recommended method was 81.71%. The proposed method was developed to assist clinicians in predicting diabetic patients using diagnostic measurements. The machine learning methods developed in this study were applied using Colab Notebook a Google Cloud Computing service.

Kaynakça

  • [1]Diabetes Overview, (2022). https://www.who.int/ne ws-room/fact-sheets/detail/diabetes
  • [2]Diabetes, (2022). https://www.who.int/health-topics /diabetes#tab=tab_1
  • [3]Güldoğan, E., Zeynep, T. U. N. Ç., Ayça, A. C. E. T., & ÇOLAK, C. (2020). Performance evaluation of different artificial neural network models in the classification of type 2 diabetes mellitus. The Journal of Cognitive Systems, 5(1), 23-32.
  • [4]Maulidah, N., Abdilah, A., Nurlelah, E., Gata, W., & Hasan, F. N. (2020). Seleksi Fitur Klasifikasi Penyakit Diabetes Menggunakan Particle Swarm Optimization (PSO) Pada Algoritma Naive Bayes. Elkom: Jurnal Elektronika dan Komputer, 13(2), 40-48.
  • [5]Tigga, N. P., & Garg, S. (2020). Prediction of type 2 diabetes using machine learning classification methods. Procedia Computer Science, 167, 706-716.
  • [6]Jakka, A., & Vakula Rani, J. (2019). Performance evaluation of machine learning models for diabetes prediction. Int. J. Innov. Technol. Explor. Eng.(IJITEE), 8(11).
  • [7]Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia computer science, 132, 1578-1585.
  • [8]Feng, T. C., Li, T. H. S., & Kuo, P. H. (2015). Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming. Applied Mathematical Modelling, 39(23-24), 7401-7419.
  • [9]Pima Indians Diabetes Database, (2022). https://data. world/data-society/pima-indians-diabetes-database
  • [10]Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • [11]Yakut Ö., Bolat E. D. (2020). Arrhythmia Diagnosis from ECG Signal Using Tree-based Machine Learning Methods. International Journal of Mathematic Engineering and Natural Sciences, 4(16),954-964.
  • [12]Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1), 3-42.
  • [13]MacKay, D. J. (1998). Introduction to Gaussian processes. NATO ASI series F computer and systems sciences, 168, 133-166.
  • [14]Yakut, Ö. (2020, November 19-21). Cloud Computing Based Voting Classifier Method Used For Survival Prediction of Heart Failure Patients. International Conference on Engineering Technologies, Konya-Turkey. https://icente.selcuk. edu.tr/
  • [15]Yakut, Ö. (2020, November 28-29). Comparison of Clustering Methods For Early Stage Diabetes Risk Prediction Using Cloud Computing. International Black Sea Coastline Countries Symposium 5, Zonguldak-Turkey.
  • [16]Google Colab Notebbook, (2022). https://colab. research. google.com
  • [17]Yakut, Ö., Bolat, E. D. (2020). An Efficient Arrhythmic Heartbeat Classification Method Using ECG Morphology Based Features. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 7(13), 200-212.
  • [18]Yakut, Ö., Timuş, O., Bolat, E. D. (2016). HRV Analysis Based Arrhythmic Beat Detection Us-ing kNN Classifier. International Journal of Biomedical and Biological Engineering, 10(2), 60-63.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Önder Yakut 0000-0003-0265-7252

Yayımlanma Tarihi 31 Mart 2023
Gönderilme Tarihi 7 Ekim 2022
Kabul Tarihi 16 Mart 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Yakut, Ö. (2023). Diabetes Prediction Using Colab Notebook Based Machine Learning Methods. International Journal of Computational and Experimental Science and Engineering, 9(1), 36-41. https://doi.org/10.22399/ijcesen.1185474