INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.14, No.4, Aug. 2022

A Novel Hybrid Approach for Detection of Type-2 Diabetes in Women Using Lasso Regression and Artificial Neural Network

Full Text (PDF, 776KB), PP.11-20


Views:2   Downloads:0

Author(s)

Yogendra Singh, Mahendra Tiwari

Index Terms

Diabetes mellitus;Lasso regression;artificial neural network;Deep learning;Predictive model

Abstract

Diabetes is a life-threatening and long-lasting illness that produces high blood glucose levels. Diabetes may cause various diseases, including liver disease, blindness, amputation, urinary organ infections, etc. This research work aims to introduce a hybrid framework to enhance outcomes predictability and interoperability with reduced ill-posed problems, over-fitting problems, and class imbalance problems for diagnosing diabetes mellitus using data mining techniques. Diabetes may be recognized in many ways. One of these methods is data mining techniques. The use of data mining to medical data has yielded meaningful, significant, and effective results that may improve medical expertise and decision-making. This study suggests a hybrid technique for detecting DM that combines the lasso regression algorithm with the artificial neural network (ANN) classifier algorithm. The Lasso regression technique is used for variable selection and regularization. Because the dataset was shrunk, the computing time was considerably minimized. The ANN classifier received the Lasso regression output as an input and classified patients correctly as diabetic and non-diabetic, i.e., tested positives and negatives. The Pima Indians dataset was used in this experiment, consisting of 768 samples of female participants who are diabetic and non-diabetic. According to experimental observations, the proposed hybrid technique achieved 93% classification accuracy for predicting diabetes mellitus. The experimental results showed that our proposed method had a classification accuracy of 93% for determining whether a patient has diabetes or not. The experimental outcomes demonstrated that a hybrid data-mining approach might assist clinicians in making better diagnoses when identifying diabetes patients.

Cite This Paper

Yogendra Singh, Mahendra Tiwari, "A Novel Hybrid Approach for Detection of Type-2 Diabetes in Women Using Lasso Regression and Artificial Neural Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.4, pp.11-20, 2022. DOI:10.5815/ijisa.2022.04.02

Reference

[1]World Health Organization. Classification of Diabetes Mellitus. WHO. Geneva: 2019.

[2]Han Cho N. IDF Diabetes Atlas. 2019th ed. 2019.

[3]Swapna G, Vinayakumar R, Soman KP. Diabetes detection using deep learning algorithms. ICT Express 2018;4:243–6. https://doi.org/10.1016/J.ICTE.2018.10.005.

[4]Awotunde JB, Ayo FE, Jimoh RG, Ogundokun RO, Matiluko OE, Oladipo ID, et al. Prediction and classification of diabetes mellitus using genomic data. Intell IoT Syst Pers Heal Care 2021:235–92. https://doi.org/10.1016/B978-0-12-821187-8.00009-5.

[5]Yuvaraj N, SriPreethaa KR. Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster. Clust Comput 2017 221 2017;22:1–9. https://doi.org/10.1007/S10586-017-1532-X.

[6]Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express 2021. https://doi.org/10.1016/J.ICTE.2021.02.004.

[7]Kumari S, Kumar D, Mittal M. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int J Cogn Comput Eng 2021;2:40–6. https://doi.org/10.1016/j.ijcce.2021.01.001.

[8]Zheng T, Xie W, Xu L, He X, Zhang Y, You M, et al. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform 2017;97:120–7. https://doi.org/10.1016/j.ijmedinf.2016.09.014.

[9]Naz H, Ahuja S. Deep learning approach for diabetes prediction using PIMA Indian dataset. J Diabetes Metab Disord 2020;19:391–403. https://doi.org/10.1007/s40200-020-00520-5.

[10]Le TM, Vo TM, Pham TN, Dao SVT. A Novel Wrapper-Based Feature Selection for Early Diabetes Prediction Enhanced with a Metaheuristic. IEEE Access 2021;9:7869–84. https://doi.org/10.1109/ACCESS.2020.3047942.

[11]Sneha N, Gangil T. Analysis of diabetes mellitus for early prediction using optimal features selection. J Big Data 2019;6. https://doi.org/10.1186/s40537-019-0175-6.

[12]Pima Indians Diabetes Database | Kaggle n.d. https://www.kaggle.com/uciml/pima-indians-diabetes-database (accessed July 26, 2021).

[13]Subhash AR, Ashwin Kumar UM. Accuracy of classification algorithms for diabetes prediction. Int J Eng Adv Technol 2019;8:230–4.

[14]Soni M, Varma DS. Diabetes Prediction using Machine Learning Techniques. Int J Eng Res Technol 2020;9.

[15]Abdulaziz M, Al-Motairy B, Al-Ghamdi M, Al-Qahtani N. Building a Personalized Fitness Recommendation Application based on Sequential Information. IJACSA) Int J Adv Comput Sci Appl n.d.;12:2021.

[16]ML Studio (classic): Normalize Data - Azure | Microsoft Docs n.d. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/normalize-data (accessed July 26, 2021).

[17]Malav A, Kadam K. A hybrid approach for Heart Disease Prediction using Artificial Neural Network and K-means. Int J Pure Appl Math 2018;118:103–9.

[18]Mahboob Alam T, Iqbal MA, Ali Y, Wahab A, Ijaz S, Imtiaz Baig T, et al. A model for early prediction of diabetes. Informatics Med Unlocked 2019;16. https://doi.org/10.1016/j.imu.2019.100204.

[19]Meyer-Baese A, Schmid V. Statistical and Syntactic Pattern Recognition. Pattern Recognit Signal Anal Med Imaging 2014:151–96. https://doi.org/10.1016/B978-0-12-409545-8.00006-6.

[20]Kulkarni A, Chong D, Batarseh FA. Foundations of data imbalance and solutions for a data democracy. Data Democr Nexus Artif Intell Softw Dev Knowl Eng 2020:83–106. https://doi.org/10.1016/B978-0-12-818366-3.00005-8.

[21]Jashwanth Reddy D, Mounika B, Sindhu S, Pranayteja Reddy T, Sagar Reddy N, Jyothsna Sri G, et al. Predictive machine learning model for early detection and analysis of diabetes. Mater Today Proc 2020. https://doi.org/10.1016/j.matpr.2020.09.522.

[22]Sisodia D, Sisodia DS. Prediction of Diabetes using Classification Algorithms. Procedia Comput. Sci., vol. 132, Elsevier B.V.; 2018, p. 1578–85. https://doi.org/10.1016/j.procs.2018.05.122.

[23]Khurana G, Kumar PA. Improving Accuracy for Diabetes Mellitus Prediction Using Data Pre-Processing and Various New Learning Models. Int J Sci Res Sci Technol 2019:502–15. https://doi.org/10.32628/IJSRST196294.

[24]Mirzajani SS, salimi  siamak. Prediction and Diagnosis of Diabetes by Using Data Mining Techniques. Avicenna J Med Biochem 2018;6:3–7. https://doi.org/10.15171/ajmb.2018.02.

[25]Joshi R, Alehegn M. Analysis and prediction of diabetes diseases using machine learning algorithm: Ensemble approach. Int Res J Eng Technol 2017;04.