TARU PUBLICATIONS
Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
Powered by:Powered by

The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Statistics and Management Systems
Open Access Research Article

Diabetes prediction using feature engineering and machine learning algorithms with security

* , , , ,

* Corresponding author · click or hover a name for details

pp. 273–284Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1253XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1253
Pages:
273–284

Abstract

The prevalence of diabetes has been steadily increasing, necessitating accurate prediction models to assist in early diagnosis and proactive management. In this paper, a hybrid machine learning-based diabetes prediction model has been proposed. To evaluate the model, the dataset was subsequently divided into training and testing subsets. We used the Random Forest Classifier, Light Gradient Boosting Mechanism Classifier, Gradient Boosting Classifier, Logistic Regression, K-Nearest Neighbours (KNN) Classifier, Naive Bayes Gaussian, Decision Tree Classifier, XGBoost Classifier, and Support Vector Classifier as nine different classifiers. Several metrics were used to evaluate the models, including testing accuracy, recall score, F1 score, and precision score. We have evaluated our model on the “Pima Indian Diabetes Database”[1], which served as the main dataset, for diabetes prediction. The proposed model serves as a practical framework for researchers and practitioners interested in leveraging machine learning techniques for diabetes prediction.

Keywords

Diabetes predictionLight gradient boosting machine (LGBM)Naive Bayes (NB)Random forestLogistic regression (LR)Gradient boosting (GB)K-nearest neighbour (k-NN)XGBoostDecision tree (DT)Support vector machines (SVM)

Subject Classifications

Primary 93A30Secondary 49K15

References

[1]  Kaggle Dataset, “Pima Indian Diabetes Database”. https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database (2017). Accessed: 2022-11-25. 
[2] International Diabetes Federation, (2021). “Diabetes Facts and Figures”. https://idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html#:~:text=Diabetes%20facts%20%26%20figures,-Last%20update%3A%2009&text=In%202021%2C,low%2D%20and%20middle%2Dincome%20countries/ . Accessed: 2022-11-20.
[3] V. V. Vijayan and C. Anjali, “Prediction and diagnosis of diabetes mellitus—A machine learning approach,” in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 122–127, Trivandrum, India (2015).
[4] Md Shahin Ali et.al, “A Novel Approach for Best Parameters Selection and Feature Engineering to Analyze and Detect Diabetes: Machine Learning Insights”, BioMed Research International, vol. 2023, Article ID 8583210, 15 pages (2023). https://doi.org/10.1155/2023/8583210.
[5] S. Perveen, M. Shahbaz, “Performance analysis of data mining classification techniques to predict diabetes,” Procedia Computer Science, vol. 82, pp. 115–121 (2016).
[6] N. Nai-arun. and R. Moungmai “Comparison of classifiers for the risk of diabetes prediction. Procedia Computer Science., 69 , pp. 132-142 (2015).
[7] A. Choudhury and D. Gupta, “A survey on medical diagnosis of diabetes using machine learning,” in Recent Developments in Machine Learning and Data Analytics: IC3 2018, Springer Singapore (2019).
[8] Anil Kumar & Sandeep Kumar Sharma. Information cryptography using cellular automata and digital image processing, Journal of Discrete Mathematical Sciences and Cryptography, 25:4, 1105-1111 (2022), DOI: 10.1080/09720529.2022.2072437.
[9] Q. Zou, K. Qu, Y. Luo, D. Yin, and Y. Ju and H. Tang, “Predicting diabetes mellitus with machine learning techniques.” Frontiers in genetics 9: 515 (2018). 
[10] H. Wu, S.Yang, Z. Huang, J. He, and X. Wang, X “Type 2 diabetes mellitus prediction model based on data mining.” Informatics in Medicine Unlocked 10: 100-107 (2018).
[11] M. Somnath et.al, “Prediction of Diabetes Type-II Using a Two-Class Neural Network” Proceedings of the (2017) International Conference on Computational Intelligence, Communications, and Business Analytics, Kolkata, India, 24–25, pp. 65–72 (2017).
[12] Sandeep Kumar Sharma, Anil Kumar, and Uday Pratap Singh. Enhanced Edges Detection from Different Color Space. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (ICIMMI ‘22). Association for Computing Machinery, New York, NY, USA, Article 16, 1–6 (2023). 
[13] J. A. Hussain, I. R. White, M. J. Johnson, et al. “Development of guidelines to reduce, handle and report missing data in palliative care trials: A multi-stakeholder modified nominal group technique”, Palliative Medicine., 36(1), pp. 59-70 (2022). 
[14] Jasuja Arush and Sonia Rathee. “Emotion Recognition Using Facial Expressions.” IJIRR vol. 11, no. 3 : pp. 1-17 (2021). http://doi.org/10.4018/IJIRR.2021070101.
[15] M. K. Dahouda and I. Joe, “A Deep-Learned Embedding Technique for Categorical Features Encoding,” in IEEE Access, vol. 9, pp. 114381-114391 (2021), doi: 10.1109/ACCESS.2021.3104357. 
[16] S. N. Dhage and C. K. Raina “A review on Machine Learning Techniques” International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, vol. 4 (3), pp. 395-399 (2016). 
[17] Sharma, Sandeep Kumar, Kumar, Anil, Ashtagi, Rashmi & Jain, Rekha. OCA: An intelligent model for improving security breach of biometric based authentication systems, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1415–1425 (2023), DOI: 10.47974/JDMSC-1765. 
[18] Sharma, S.K., Kumar, A., Digital Image Transformation Using Outer Totality Cellular Automata. Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-0085-5_69.
[19] D. Mishra,et.al, “Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network” Digital Communications and Networks, vol. 9(1), pp. 125-137 (2023).
[20]  A-A.Tanvir, I. A. Khandokar, “A gradient boosting classifier for purchase intention prediction of online shoppers” Heliyon, vol. 9(4)  (2023).
[21] C. Cortes and V. Vapnik, “Support-vector networks”, Mach. Learn., 20 (3) , pp. 273-297 (1995).

Views: 164Downloads: 9Citations: 0