Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Tsehay Admassu Assegie, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan, Napa Komal Kumar

Abstract


Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned.  With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

Keywords


Optimization; Machine Learning; Automated Diagnosis; Parameter Tuning; Classification.

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References


R. N. Patil. S. Rawandale, N. Rawandale, U. Rawandale, and S. Patil, “An efficient stacking based NSGA-II approach for predicting type 2 diabetes,” International Journal of Electrical and Computer Engineering, vol. 13, no. 1, pp. 1015-1023, 2023, doi: 10.11591/ijece.v13i1.pp1015-1023.

Q. Zou, K. Qu, Y. Luo, D. Yin, Y. Ju, and H. Tang, H, “Predicting Diabetes Mellitus with Machine Learning Techniques,” Frontiers in genetics, vol. 9, no. 515, 2018, doi: 10.3389/fgene.2018.00515.

R. Jader and S. Aminifar, “Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach,” Applied Computational Intelligence and Soft Computing, vol. 2022, 2022, doi: 10.1155/2022/9749579.

E. Sabitha and M. Durgadevi, “Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, 2022.

A. H. Osman and H. M. Aljahdali, “Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM,” International Journal of Advanced Computer Science and Applications, vol. 8, no. 1, 2017.

A. Dutta et al., “Early Prediction of Diabetes Using an Ensemble of Machine Learning Models,” International Journal of Environmental Research and Public Health, vol. 19, no. 19, p. 12378, 2022, doi: 10.3390/ijerph191912378.

L. Shrinivasan, R. Verma, and M. D. Nandeesh, “Early prediction of diabetes diagnosis using hybrid classification techniques,” IAES International Journal of Artificial Intelligence, vol. 12, no. 3, pp. 1139-1148, 2023, doi: 10.11591/ijai.v12.i3.pp1139-1148.

Y. N. Chan et al., “A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails,” Scientific Reports, vol. 13, no. 1, p. 4184, 2023, doi: 10.1038/s41598-023-31270-y.

H. F. Ahmad, H. Mukhtar, H. Alaqail, M. Seliaman, and A. Alhumam, “Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning,” Applied Sciences, vol. 11, no. 3, p. 1173, 2021, doi: 10.3390/app11031173.

H. Naz and S. Ahuja, “Deep learning approach for diabetes prediction using PIMA Indian dataset,” Journal of Diabetes & Metabolic Disorders, vol. 19, pp. 391-403, 2020, doi: 10.1007/s40200-020-00520-5.

A. Bansal and A. Singhrova, “Performance Analysis of Supervised Machine Learning Algorithms for Diabetes and Breast Cancer Dataset,” 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 137-143, 2021, doi: 10.1109/ICAIS50930.2021.9396043.

R. Saxena, “Role of K-nearest neighbour in detection of Diabetes Mellitus,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 10, pp. 373-376, 2021.

M. T. Alasaady, T. N. M. Aris, N. M. Sharef, and H. Hamdan, “A proposed approach for diabetes diagnosis using neuro-fuzzy technique,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 6, pp. 3590-3597, 2022, doi: 10.11591/eei.v11i6.4269.

N. AlRefaai and S. Z AlRashid, “Classification of gene expression dataset for type 1 diabetes using machine learning methods,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 5, 2023, pp. 2986-2992, doi: 10.11591/eei.v12i5.4322.

P. A. Rajendra and S. Latif, “Prediction of diabetes using logistic regression and ensemble techniques,” Computer Methods and Programs in Biomedicine Update, vol. 1, p. 100032, 2021, doi: 10.1016/j.cmpbup.2021.100032.

S. Khairunnisa, S. Suyanto, and P. Eko Yunanto, “Removing Noise, Reducing dimension, and Weighting Distance to Enhance kk-Nearest Neighbors for Diabetes Classification,” 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 471-475, 2020, doi: 10.1109/ISRITI51436.2020.9315515.

M. Ahsan, P. Mahmud, P. K. Saha, K. D. Gupta, and Z. Siddique, “Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance,” Technologies, vol. 9, no. 52, 2021, doi: 10.3390/ technologies9030052.

T. A. Assegie, V. Elanangai, J. S. Paulraj, M. Velmurugan, and D. F. Devesan, “Evaluation of feature scaling for improving the performance of supervised learning methods,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 3, pp. 1833-1838, 2023, doi: 10.11591/eei.v12i3.5170.

P. Ferreira, D. C. Le, and N. Zincir-Heywood, “Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection,” 2019 15th International Conference on Network and Service Management (CNSM), pp. 1-7, 2019, doi: 10.23919/CNSM46954.2019.9012708.

V. N. G. Raju, K. P. Lakshmi, V. M. Jain, A. Kalidindi, and V. Padma, “Study the Influence of Normalization/Transformation process on the Accuracy of Supervised Classification,” 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 729-735, 2020, doi: 10.1109/ICSSIT48917.2020.9214160.

M. J. Pendekal and S. Gupta, “An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy,” Indonesian Journal of Electrical Engineering and Informatics, vol. 10, no. 1, pp. 60-71, 2022, doi: 10.52549/ijeei.v10i1.3522.

N. Ahmed et al., “Machine learning based diabetes prediction and development of smart web application,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 229-241, 2021, doi: 10.1016/j.ijcce.2021.12.001.

T. A. Assegie and P. S. Nair, “The Performance of Different Machine Learning Models on Diabetes Prediction,” International Journal of Scientific & Technology Research, vol. 9, no. 1, 2020.

M. F. Faruque, A. Asaduzzaman, and I. H. Sarker, “Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-4, 2019, doi: 10.1109/ECACE.2019.8679365.

H. Butt, I. Khosa, and M. A. Iftikhar, “Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients,” Diagnostics, vol. 13, no. 3, p. 340, 2023, doi: 10.3390/ diagnostics13030340.

N. P. Miriyala et al., “Diagnostic Analysis of Diabetes Mellitus Using Machine Learning Approach,” Revue d'Intelligence Artificielle, vol. 36, no. 3, pp. 347-352, 2022, doi: 10.18280/ria.360301.

B. C. Kusumaatmajaa and H. Thamrin, “Web-Based Prediction of Potential Diabetes Outbreaks Using Django with the KNN Algorithm,” AIP Conference Proceedings, vol. 2727, no. 1, p. 040004, 2023, doi: 10.1063/5.0141932.

O. Altay, M. Ulas, and K. E. Alyamac, “Prediction of the Fresh Performance of Steel Fiber Reinforced Self-Compacting Concrete Using Quadratic SVM and Weighted KNN Models,” in IEEE Access, vol. 8, pp. 92647-92658, 2020, doi: 10.1109/ACCESS.2020.2994562.

R. Patil and S. Tamane, “A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes,” International Journal of Electrical and Computer Engineering, vol. 8, no. 5, pp. 3966-3975, 2018, doi: https://doi.org/10.11591/ijece.v8i5.pp3966-3975.

Y. N. Fuadah, M. A. Pramudito, and K. M. Lim, “An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning,” Bioengineering, vol. 10, no. 1, p. 45, 2023, doi: 10.3390/bioengineering10010045.

S. Ambesange, R. Nadagoudar, R. Uppin, V. Patil, S. Patil, and S. Patil, “Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques,” 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), pp. 1-6, 2020, doi: 10.1109/B-HTC50970.2020.9297949.

D. M. Belete and M. D. Huchaiah, “Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results,” International Journal of Computers and Applications, vol. 44, no. 9, pp. 875-886, 2021, doi: 10.1080/1206212X.2021.1974663.

D. U. Ozsahin, M. Taiwo Mustapha, A. S. Mubarak, Z. S. Ameen, and B. Uzun, “Impact of feature scaling on machine learning models for the diagnosis of diabetes,” 2022 International Conference on Artificial Intelligence in Everything (AIE), pp. 87-94, 2022, doi: 10.1109/AIE57029.2022.00024.

S. A. Alalwan et al., “Diabetic analytics: proposed conceptual data mining approaches in type 2 diabetes dataset,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 14, no. 1, pp.88-95, 2019, doi: 10.11591/ijeecs.v14.i1.pp88-95.

M. Panda, D. P. Mishra, S. M. Patro, and S. R. Salkut, “Prediction of diabetes disease using machine learning algorithms,” IAES International Journal of Artificial Intelligence, vol. 11, no. 1, pp. 284-290, 2022, doi: 10.11591/ijai.v11.i1.pp284-290.

K. Yothapakdee, S. Charoenkhum, and T. Boonnuk, “Improving the efficiency of machine learning models for predicting blood glucose levels and diabetes risk,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 27, no. 1, pp. 555-562, 2022, doi: 10.11591/ijeecs.v27.i1.pp555-562.

H. R. Ismail and M. M. Hassan, “Bayesian deep learning methods applied to diabetic retinopathy disease: a review,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 2, pp. 1167-1177, 2023, doi: 10.11591/ijeecs.v30.i2.pp1167-1177.

G. Nikhila, T. Bhuvan, and R. Jerrard, “Time series prediction of personalized insulin dosage for type 2 diabetics,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 2, pp. 1080-1087, 2023, doi: 10.11591/ijeecs.v31.i2.pp1080-1087.

Y. Tan, H. Chen, J. Zhang, R. Tang, and P. Liu, “Early Risk Prediction of Diabetes Based on GA-Stacking,” Applied Science, vol. 12, no. 2, p. 632, 2022, doi: 10.3390/app12020632.

T. Alghamdi, “Prediction of Diabetes Complications Using Computational Intelligence Techniques,” Applied Science, vol. 13, no. 5, p. 3030 2023, doi: 10.3390/app13053030.

O. AlShorman, B. AlShorman, and F. Alkahtani, “A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes,” International Journal of Electrical and Computer Engineering, vol. 11, no. 1, pp. 646-653, 2021, doi: 10.11591/ijece.v11i1.pp646-653.

A. Yasar, “Data Classification of Early-Stage Diabetes Risk Prediction Datasets and Analysis of Algorithm Performance Using Feature Extraction Methods and Machine Learning Techniques,” International Journal of Intelligent Systems and Applications In Engineering, vol. 9, no. 4, pp. 273-281, 2021.

J. J. Sonia, P. Jayachandran, A. Q. Md, S. Mohan, A. K. Sivaraman, and K. F. Tee, “Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm,” Diagnostics, vol. 13, no. 4, p. 723, 2023, doi: 10.3390/diagnostics13040723.

J. R. Raut, Y. Sharma, and V. D. Shinde, “Performance Evaluation of Various Supervised Machine Learning Algorithms for Diabetes Prediction,” European Journal of Molecular & Clinical Medicine, vol. 7, no. 8, 2020.

S. P. Menon et al., “An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications,” Sensors, vol. 23, no. 6, p. 3004, 2023, doi: 10.3390/s23063004.

H. B. Kibria, M. Nahiduzzaman, O. F. Goni, M. Ahsan, and J. Haider, “An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI,” Sensors, vol. 22, no. 19, p. 7268, 2022, doi: 10.3390/s22197268.

D. S. Sisodia and R. Agrawal, “Data Imputation-Based Learning Models for Prediction of Diabetes,” 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 966-970, 2020, doi: 10.1109/DASA51403.2020.9317070.

J. Ramesh, R. Aburukba, and A. Sagahyroon, “A remote healthcare monitoring framework for diabetes prediction using machine learning,” Healthcare Technology Letters, vol. 8, no. 3, pp. 45-57, April 2021, doi: 10.1049/htl2.12010.

H. Lu, J. Hirst, J. Yang, L. Mackillop, and D. Clifton, “Standardizing the assessment of caesarean birth using an oxford caesarean prediction score for mothers with gestational diabetes,” Healthcare Technology Letters, vol. 9, no. 1-2, pp. 1-8, 2022, doi: 10.1049/htl2.12022.

I. Tasin, T. U. Nabi, S. Islam, and R. Khan, “Diabetes prediction using machine learning and explainable AI techniques,” Healthcare Technology Letters, vol. 10, no. 1-2, pp. 1-10, 2022, doi: 10.1049/htl2.12039.

B. Premamayud, K. Muralikrishna, and K. Pramodh “Diabetes Prediction Using Machine Learning KNN -Algorithm Technique,” International Journal of Innovative Science and Research Technology, vol. 7, no. 5, May 2022.

A. S. Hassan, I. Malaserene, and A. A. Leema, “Diabetes Mellitus Prediction using Classification Techniques,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 5, March 2020.

A. K. Gangwar and V. Ravi, “Diabetic retinopathy detection using transfer learning and deep learning,” In Evolution in Computational Intelligence: Frontiers in Intelligent Computing: Theory and Applications (FICTA 2020), vol. 1, pp. 679-689, 2021.

A. Sharma, S. Shinde, I. I. Shaikh, M. Vyas, and S. Rani, “Machine Learning Approach for Detection of Diabetic Retinopathy with Improved Pre-Processing,” 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 517-522, 2021, doi: 10.1109/ICCCIS51004.2021.9397115.

S. Patikar, P. Saha, S. Neogy, and C. Chowdhury, “An Approach towards prediction of Diabetes using Modified Fuzzy K Nearest Neighbor,” 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), pp. 73-76, 2020, doi: 10.1109/GUCON48875.2020.9231066.

S. M. A. Huda, I. J. Ila, S. Sarder, M. Shamsujjoha, and M. N. Y. Ali, “An Improved Approach for Detection of Diabetic Retinopathy Using Feature Importance and Machine Learning Algorithms,” 2019 7th International Conference on Smart Computing & Communications (ICSCC), pp. 1-5, 2019, doi: 10.1109/ICSCC.2019.8843676.

M. Nilashi, S. Samad, E. Yadegaridehkordi, A. Alizadeh, and E. Akbari, “Early Detection of Diabetic Retinopathy Using Ensemble Learning Approach,” Journal of Soft Computing & Decision Support Systems, vol. 6, no. 2, 2019.

P. S. Kumar, A. K. K, S. Mohapatra, B. Naik, J. Nayak, and M. Mishra, “CatBoost Ensemble Approach for Diabetes Risk Prediction at Early Stages,” 2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), pp. 1-6, 2021, doi: 10.1109/ODICON50556.2021.9428943.

M. Hassan, S. Mollick, and F. Yasmin, “An unsupervised cluster-based feature grouping model for early diabetes detection,” Healthcare Analytics, vol. 2, p. 100112, 2022, doi: 10.1016/j.health.2022.100112.

S. H. Shaker, A. Farah, and A. Mahdi, “Diagnosis of Diabetes Mellitus Based Combined of Feature Selection Methods,” Journal of Theoretical and Applied Information Technology, vol. 100, no. 13, July 2022.




DOI: https://doi.org/10.18196/jrc.v4i4.18412

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