An Approach for the Prediction of Diabetes via Relief Feature Selection

Authors: Nebi Gedik

Abstract:

One of the most common chronic diseases in the world, diabetes is brought on by insufficient insulin production by the pancreas or by inefficient insulin utilization by the body. The disease is linked to the interplay of lifestyle, behavioral and medical circumstances, demographics, and genetic risk factors. Early disease detection is crucial for helping medical professionals with diagnosis or prognosis as well as for creating a successful preventative strategy. Machine learning techniques are utilized for this purpose in order to identify diabetes from medical records. Finding the characteristics or features that provide the best prediction of classification for diabetes detection is the aim of this study. The performance of each feature is compared using the linear discriminant analysis and k-nearest neighbor classifiers. The feature that yields the best classification results has been determined.

Keywords: Diabetes, relief feature selection, k-nearest neighbor classifiers, lenear discriminant analysis.

References:

[1] Z. Punthakee, R. Goldenberg, P. Katz, “Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome,” Can. J. diabetes, vol.42, pp. 10–15, 2018.
[2] R. Vaishali, R. Sasikala, S. Ramasubbareddy, S. Remya, S. Nalluri, “Genetic algorithm-based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset,” In 2017 international conference on computing networking and informatics (ICCNI), 2017, pp. 1–5.
[3] P. Saeedi, I. Petersohn, P. Salpea, B. Malanda, S. Karuranga, N. Unwin, ... & IDF Diabetes Atlas Committee, “Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas,” Diabetes research and clinical practice, vol. 157, pp.107843, 2019.
[4] International Diabetes Federation, Facts & figures, (accessed Aug. 27, 2024). Available online: https://www.who.int/health-topics/diabetes#tab=tab_1.
[5] J. Chaki, S. T. Ganesh, S. K. Cidham, & S. A. Theertan, “Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp.3204-3225, 2022.
[6] R. Katarya, S. Jain, “Comparison of different machine learning models for diabetes detection,” In: Proc. 2020 IEEE int. Conf. Adv. Dev. Electr. Electron. Eng. ICADEE 2020. Institute of Electrical and Electronics Engineers Inc.; 2020, pp. 1-5.
[7] C. C. Olisah, L. Smith, & M. Smith, “Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective,” Computer Methods and Programs in Biomedicine, vol. 220, pp.106773, 2022.
[8] S. Saxena, D. Mohapatra, S. Padhee, & G. K. Sahoo, “Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms,” Evolutionary Intelligence, pp.1-17, 2023.
[9] S. S. Sivasankari, J. Surendiran, N. Yuvaraj, M. Ramkumar, C. N. Ravi, & R. G. Vidhya, “Classification of diabetes using multilayer perceptron,” In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), April 2022, pp. 1-5. IEEE.
[10] L. J. Muhammad, E. A. Algehyne, & S. S. Usman, “Predictive supervised machine learning models for diabetes mellitus,” SN Computer Science, vol. 1, no. 5, p.240, 2020.
[11] I. Tasin, T. U. Nabil, S. Islam, and R. Khan, “Diabetes prediction using machine learning and explainable AI techniques,” Healthcare Technol. Lett. vol. 10, pp.1–10, 2023.
[12] C. Lyngdoh, N. A. Choudhury, & S. Moulik, “Diabetes disease prediction using machine learning algorithms,” in Proceedings 2020 IEEE-EMBS Conf. on Biomedical Engineering and Sciences (IECBES) (Langkawi Island, Malaysia), 2021, pp. 517–521.
[13] V. Rupapara, F. Rustam, A. Ishaq, E. Lee, and I. Ashraf, “Chi-square and PCA based feature selection for diabetes detection with ensemble classifier,” Intell. Autom. Soft Comput., vol. 36, no. 2, pp. 1931–1949, 2023.
[14] U. M. Butt, S. Letchmunan, M. Ali, F. H. Hassan, A. Baqir, & H. H. R. Sherazi, “Machine learning based diabetes classification and prediction for healthcare applications,” Journal of healthcare engineering, vol. 2021, no. 1, pp. 9930985, 2021.
[15] K. Kira, L. A. Rendell, “The feature selection problem: traditional methods and a new algorithm”, In proceedings of the tenth national conference on Artificial intelligence, vol. 2, 1992a, pp. 129–134.
[16] K. Kira, L. A. Rendell, “A practical approach to feature selection,” In: Proceedings of the Ninth International Workshop on Machine Learning, 1992b, pp. 249–256.
[17] S. F. Rosario, and K. Thangadurai. "RELIEF: feature selection approach," International journal of innovative research and development, vol. 4., No. 11, pp. 2018-224, 2015.
[18] Pima Indians Diabetes Dataset, (accessed Aug. 27, 2024). Online. Available: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database
[19] E. Fix, J. L. Hodges, “Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties,” International Statistical Review/ Revue Internationale de Statistique, vol. 57, no. 3, pp. 238, 1989.
[20] T. M. Cover, P. Hart, “Nearest neighbor pattern classification,” IEEE Trans Inf Theory, vol. 13, no. 1, pp. 21–27, 1967.
[21] N. Ali, D. Neagu, & P. Trundle, “Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets,” SN Applied Sciences, vol. 1, pp. 1-15, 2019.
[22] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugen., vol. 7, pp. 179–188, 1936.
[23] S. Balakrishnama, & A. Ganapathiraju, “Linear discriminant analysis-a brief tutorial,” Institute for Signal and information Processing, vol. 18, pp. 1-8, 1998.
[24] D. Lopez-Bernal, D. Balderas, P. Ponce, & A. Molina, “Education 4.0: teaching the basics of KNN, LDA and simple perceptron algorithms for binary classification problems,” Future Internet, vol. 13, no. 8, pp. 193, 2021.
[25] P. Xanthopoulos, P. M. Pardalos, T. B.Trafalis, “Linear Discriminant Analysis,” In: Robust Data Mining. Springer Briefs in Optimization. Springer, New York, NY, 2013
[26] A. Mucherino et al. “K-nearest neighbor classification,” Data mining in agriculture, pp. 83-106, 2009.
[27] How Dimension Reduction works (accessed Oct. 18, 2024) https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/how-dimension-reduction-works.htm
[28] T. M. Alam, M. A. Iqbal, Y. Ali, A. Wahab, S. Ijaz, T. I. Baig, & Z. Abbas, “A model for early prediction of diabetes,” Informatics in Medicine Unlocked, vol. 16, pp. 100204, 2019.
[29] D. Sisodia, D. S. Sisodia, “Prediction of diabetes using classification algorithms,” Procedia computer science, vol. 132, pp. 1578-85, 2018.
[30] N. P. Tigga, S. Garg, “Predicting type 2 diabetes using logistic regression,” In Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems: MCCS, pp. 491-500, Springer Singapore, 2019.
[31] S. Larabi-Marie-Sainte, L. Aburahmah, R. Almohaini, T. Saba, “Current techniques for diabetes prediction: review and case study,” Applied Sciences, vol. 9, no. 21, pp. 4604, 2019.
[32] P. Rajendra, S. Latifi, “Prediction of diabetes using logistic regression and ensemble techniques,” Computer Methods and Programs in Biomedicine Update, vol. 1, pp.100032, 2021.
[33] R. P. Alluri, R. Hemavathy, “Diabetes Prediction Using Ensemble Techniques,” International Journal of Applied Engineering Research, vol. 16, no. 5, pp. 410-5, 2021.
[34] M. O. Edeh, O. I. Khalaf, C. A. Tavera, S. Tayeb, S. Ghouali, G. M. Abdulsahib, N. E. Richard-Nnabu, A. Louni, “A Classification Algorithm-Based Hybrid Diabetes Prediction Model,” Front. Public Health, vol. 10, pp. 829519, 2022.
[35] C. Azad, B. Bhushan, R. Sharma, A. Shankar, K. K. Singh, and A. Khamparia, “Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus,” In Multimedia Systems, Springer Science and Business Media Deutschland GmbH, Aug. 2022, pp. 1289–1307.
[36] S. Barik, S. Mohanty, S. Mohanty, and D. Singh, “Analysis of prediction accuracy of diabetes using classifier and hybrid machine learning techniques,” In Smart Innovation, Systems and Technologies, Springer Science and Business Media Deutschland GmbH, 2021, pp. 399–409.