Predict Diabetes Using Voting Classifier and Hyper Tuning Technique
https://doi.org/10.24017/Science.2022.2.10
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Today, diabetes is one of the most common chronic diseases in the world due to the people’s sedentary lifestyle which led to many health issues like heart attack, kidney frailer and blindness. Additionally, most of the people are unrealizable about the early-stage diabetes symptoms to prevent it. The above reasons were encouraging to develop a diabetes prediction system using machine learning techniques. The Pima Indian Diabetes Dataset (PIDD) was utilized for this framework as it is common and appropriate dataset in .CSV format. While there were not any duplicate or null values, however, some zero values were replaced, four outlier records were removed and data standardization were performed in the dataset. In addition, this project methodology divided into two phases of model selection. In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. Then six different algorithms (Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and Naïve Bayes) were applied. In the second phase, the four best performed algorithms (with best estimated parameters for each of them) were chosen and used as an input for the voting classifier, because it applies to find the best algorithm between a group of multiple options. The result was satisfying, and Random Forest was achieved 98.69% in second stage, while its accuracy level was 81.04% in the previous one and it utilized to predict diabetes via a simple graphic user interface.
Keywords:
Diabetic Prediction, Graphical User Interface, Hyper Tuning, Machine Learning Algorithm, Voting Classifier
References
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https://doi.org/10.3390/ijms21176275
[3] R. Singla,A. Singla, Y. Gupta and S. Kalra,"Artificial Intelligence/Machine Learning in Diabetes Care," Indian Journal of Endocrinology and Metabolism, vol. 23, issue 4, pp. 495-497, 2019.
https://doi.org/10.4103/ijem.IJEM_228_19
[4] M. Makroum, M. Adda, A. Bouzouane and H. Ibrahim,"Machine Learning and Smart Devices for Diabetes Management: Systematic Review,"Sensors, vol. 22, issue 5, pp.1843, 2022.
https://doi.org/10.3390/s22051843
[5] Y. Jian, M. Pasquier, A. Sagahyroon and F. Aloul," A Machine Learning Approach to Predicting Diabetes Complications," Healthcare, vol. 9, issue 12, pp. 1712, 2021.
https://doi.org/10.3390/healthcare9121712
[6] A. Tuppad and Sh.D. Patil,"Machine learning for diabetes clinical decision support: a review," Advances in Computational Intelligence, vol. 2, issue 22, pp.2022, 2022.
https://doi.org/10.1007/s43674-022-00034-y
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https://doi.org/10.1038/s41598-020-71908-9
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https://doi.org/10.3390/diagnostics12010212
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https://doi.org/10.1016/j.cosrev.2020.100315
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https://doi.org/10.1155/2022/6521532
[17] R. Krishnamoorthi, Sh. Joshi, H.Z. Almarzouki, P.K. Shukla, A. Rizwan, C. Kalpana and B. Tiwari,"A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques," Journal of Healthcare Engineering, vol. 2022, pp.1684017, 2022.
https://doi.org/10.1155/2022/1684017
[18] S. R, S. M, M.K. Hasan, R.A. Saeed, S.A. Alsuhibany and S. Abdel-Khalek, "An Empirical Model to Predict the Diabetic Positive Using Stacked Ensemble Approach," Front. Public Health, vol. 9, pp.792124, 2022.
https://doi.org/10.3389/fpubh.2021.792124
[19] B.S. Ahamed, M.S. Arya and A.O. Nancy V, "Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning Classifiers and Techniques," Front. Comput. Sci, vol. 4, pp. 835242,2022.
https://doi.org/10.3389/fcomp.2022.835242
[20] F. Bano, M. K and R. MadanaMohana, "Predict Diabetes Mellitus Using Machine Learning Algorithms,"Journal of Physics: Conference Series, vol. 2089, pp.012002, 2021.
https://doi.org/10.1088/1742-6596/2089/1/012002
[21] O. Llaha and A. Rista,"Prediction and Detection of Diabetes using Machine Learning," CEUR Workshop Proceedings, vol. 2872, pp. 94-102, 2021.
[22] A.K. Jaggi, A. Sharma, N. Sharma, R. Singh and P.S. Chakraborty,"Diabetes Prediction Using Machine Learning," Intelligent System, vol. 185, pp. 383-392, 2021.
https://doi.org/10.1007/978-981-33-6081-5_34
[23] N. Ahmed, K. Hamakarim and Z.Kakarash,"A Temporal and Social Network-based Recommender using Graph Clustering," Passer Journal, vol. 4, issue 2, pp. 180-18, 2022.
https://doi.org/10.24271/psr.2022.344758.1134
[24] R. Patra and B. Khuntia,"Analysis and Prediction Of Pima Indian Diabetes Dataset Using SDKNN Classifier Technique," IOP Conference Series: Materials Science and Engineering, vol. 1070, pp. 012059, 2021.
https://doi.org/10.1088/1757-899X/1070/1/012059
[25] E. Elgeldawi, A. Sayed, A. Galal and A. Zaki,"Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis," Informatics, vol. 8, issue 4, pp.79, 2021.
https://doi.org/10.3390/informatics8040079
[26] Y. Zhao, R. Zhang and X. Li,"AutoDESS: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection,"arXiv, vol. 2, pp. 2201.00207, 2022.
[27] M. Soni and S.Varma, "Diabetes Prediction using Machine Learning Techniques," International Journal of Engineering Research & Technology (IJERT), vol. 9, issue 9, pp. 921-925, 2022.
[28] B. Hassan and T. Rashid, "A multi-disciplinaryEnsemble Algorithm for Clustering Heterogonous Datasets," Neural Computing and Applications, vol. 33, pp. 10987-11010, 2021.
https://doi.org/10.1007/s00521-020-05649-1
[29] A. Baratloo, M. Hosseini, A. Negida and G. El Ashal, "Part 1: Simple Definition and Calculation of Accuracy," Sensitivity and Specificity, Emergency (Tehran),vol.3, issue 2, pp. 48-9, 2015.
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[31] A. Dhruv, R. Patel and N. Doshi, "Python:The Most Advanced Programming Languages for Computer Science Application," Science and Technology Publications, Lda, pp.292-299, 2021.
https://doi.org/10.5220/0010307902920299
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https://doi.org/10.1109/JCDL.2017.7991618
[35] JetBrains, "Download PyCharm," jetbrains.com, Dec. 11,2022. [Online]. Available: https://www.jetbrains.com/pycharm/download/#section=windows[Accessed: March 11, 2022].
[36] ANACONDA, "Installers," anaconda.org, Dec. 11,2022. [Online]. Available: https://anaconda.org/anaconda/django [Accessed: March 11,2022].
[37] B. Hassan, T. Rashid and H. Hamarashid, "A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star," Computer in Biology and Medicine, vol. 138, pp. 104866, 2021.
https://doi.org/10.1016/j.compbiomed.2021.104866
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[1]
C. A. Kamal and M. A. Atiyah, “Predict Diabetes Using Voting Classifier and Hyper Tuning Technique”, KJAR, vol. 7, no. 2, pp. 115–130, Jan. 2023, doi: 10.24017/Science.2022.2.10.
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15-01-2023
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Copyright (c) 2023 Chra Ali Kamal, Manal Ali Atiyah
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