A survey on prediction of diabetes using classification algorithms

• Autorzy: Khanwalkar A. , Soni R.

Identyfikatory

DOI

10.5604/01.3001.0014.8490

• Języki publikacji: EN

Abstrakty

EN

Purpose: Diabetes is a chronic disease that pays for a large proportion of the nation's healthcare expenses when people with diabetes want medical care continuously. Several complications will occur if the polymer disorder is not treated and unrecognizable. The prescribed condition leads to a diagnostic center and a doctor's intention. One of the real-world subjects essential is to find the first phase of the polytechnic. In this work, basically a survey that has been analyzed in several parameters within the poly-infected disorder diagnosis. It resembles the classification algorithms of data collection that plays an important role in the data collection method. Automation of polygenic disorder analysis, as well as another machine learning algorithm. Design/methodology/approach: This paper provides extensive surveys of different analogies which have been used for the analysis of medical data, For the purpose of early detection of polygenic disorder. This paper takes into consideration methods such as J48, CART, SVMs and KNN square, this paper also conducts a formal surveying of all the studies, and provides a conclusion at the end. Findings: This surveying has been analyzed on several parameters within the poly-infected disorder diagnosis. It resembles that the classification algorithms of data collection plays an important role in the data collection method in Automation of polygenic disorder analysis, as well as another machine learning algorithm. Practical implications: This paper will help future researchers in the field of Healthcare, specifically in the domain of diabetes, to understand differences between classification algorithms. Originality/value: This paper will help in comparing machine learning algorithms by going through results and selecting the appropriate approach based on requirements.

Słowa kluczowe

EN

diabetes; diabetes prediction; algorithm; data mining; machine learning

PL

cukrzyca; algorytm; eksploracja danych; uczenie maszynowe

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2021
• Tom: Vol. 104, nr 2
• Strony: 77--84
• Opis fizyczny: Bibliogr. 36 poz., rys., wykr.

Twórcy

autor

Khanwalkar A.

• Department of Computer Science Engineering, ITM Group of Institution Gwalior, India

• https://orcid.org/0000-0003-3464-5408

autor

Soni R.

• Head of the Department of Computer Science Engineering, ITM Group of Institution Gwalior, India

Bibliografia

• [1] D. Shetty, K. Rit, S. Shaikh, N. Patil, Diabetes disease prediction using data mining, Proceedings of the International Conference on Innovations in Information, Embedded and Communication Systems “ICIIECS”, Coimbatore, India, 2017, 1-5. DOI: https://doi.org/10.1109/ICIIECS.2017.8276012
• [2] R. Mirshahvalad, N.A. Zanjani, Diabetes prediction using ensemble perceptron algorithm, Proceedings of the 9th International Conference on Computational Intelligence and Communication Networks “CICN”, Girne, Northern Cyprus, 2017, 190-194. DOI: https://doi.org/10.1109/CICN.2017.8319383
• [3] A. Iyer, S. Jeyalatha, R. Sumbaly, Diagnosis of diabetes using classification mining techniques, International Journal of Data Mining and Knowledge Management Process 5/1 (2015) 1-14. DOI: https://doi.org/10.5121/ijdkp.2015.5101
• [4] V.P. Kumar, V. Lakshmi, A Data Mining Approach for Prediction and Treatment of diabetes Disease, International Journal of Science Inventions Today 3/1 (2014) 73-79.
• [5] R. Sanakal, T. Jayakumari, Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine, International Journal of Computer Trends and Technology 11/2 (2014) 94-98. DOI: https://doi.org/10.14445/22312803/IJCTT-V11P120
• [6] B.L. Shivakumar, S. Alby, A Survey on Data-Mining Technologies for Prediction and Diagnosis of Diabetes, Proceedings of the International Conference on Intelligent Computing Applications, Coimbatore, India, 2014, 167-173. DOI: https://doi.org/10.1109/ICICA.2014.44
• [7] P. Yasodha, M. Kannan, Analysis of a Population of Diabetic Patients Databases in WEKA Tool, International Journal of Scientific and Engineering Research 2/5 (2011) 1-5.
• [8] B. Alić, L. Gurbeta, A. Badnjević, Machine learning techniques for classification of diabetes and cardiovascular diseases, Proceedings of the 6th Mediterranean Conference on Embedded Computing “MECO”, Bar, 2017, 1-4. DOI: https://doi.org/10.1109/MECO.2017.7977152
• [9] R.M. Khalil, A. Al-Jumaily, Machine learning based prediction of depression among type 2 diabetic patients, Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering “ISKE”, Nanjing, China, 2017, 1-5. DOI: https://doi.org/10.1109/ISKE.2017.8258766
• [10] H. Walia, A. Rana, V. Kansal, A Naïve Bayes Approach for working on Gurmukhi Word Sense Disambiguation, Proceedings of the 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) “ICRITO”, Noida, India, 2017, 432-435. DOI: https://doi.org/10.1109/ICRITO.2017.8342465
• [11] S. Vijiyarani S. Sudha, Disease Prediction in Data Mining Technique - A Survey, International Journal of Computer Applications and Information Technology II/I (2013) 17-21.
• [12] P. Radha, B. Srinivasan, Predicting Diabetes by sequencing the various Data Mining Classification Techniques, International Journal of Innovative Science, Engineering and Technology 1/6 (2014) 334-339.
• [13] N.J. Vispute, D.K. Sahu, A. Rajput, A survey on naive Bayes Algorithm for Diabetes Data Set Problems, International journal for research in Applied Science and Engineering Technology 3/XII (2015) 487-494.
• [14] W. Chen, S. Chen, H. Zhang, T. Wu, A hybrid prediction model for type 2 diabetes using K-means and decision tree, proceedings of the 8th IEEE International Conference on Software Engineering and Service Science “ICSESS”, Beijing, China, 2017, 386-390. DOI: https://doi.org/10.1109/ICSESS.2017.8342938
• [15] X. Sun, X. Yu, J. Liu, H. Wang, Glucose prediction for type 1 diabetes using KLMS algorithm, Proceedings of the 36th Chinese Control Conference “CCC”, Dalian, China, 2017, 1124-1128. DOI: https://doi.org/10.23919/ChiCC.2017.8027498
• [16] Purushottam, K. Saxena, R. Sharma, Diabetes mellitus prediction system evaluation using C4.5 rules and partial tree, Proceedings of the 4th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) “ICRITO”, Noida, India, 2015, 1-6. DOI: https://doi.org/10.1109/ICRITO.2015.7359272
• [17] S.A. Saji, K. Balachandran, Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction, Proceedings of the International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, 2015, 201-206. DOI: https://doi.org/10.1109/ICACEA.2015.7164695
• [18] A. Anand, D. Shakti, Prediction of diabetes based on personal lifestyle indicators, Proceedings of the 1st International Conference on Next Generation Computing Technologies “NGCT”, Dehradun, India, 2015, 673-676. DOI: https://doi.org/10.1109/NGCT.2015.7375206
• [19] S. Bae, T. Park, Risk prediction using common and rare genetic variants: Application to Type 2 diabetes, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine “BIBM”, Kansas City, MO, USA, 2017, 1757-1760. DOI: https://doi.org/10.1109/BIBM.2017.8217926
• [20] A. Negi, V. Jaiswal, A first attempt to develop a diabetes prediction method based on different global datasets, Proceedings of the 4th International Conference on Parallel, Distributed and Grid Computing “PDGC”, Waknaghat, India, 2016, 237-241. DOI: https://doi.org/10.1109/PDGC.2016.7913152
• [21] N. Douali, J. Dollon, M. Jaulent, Personalized prediction of gestational Diabetes using a clinical decision support system, Proceedings of the IEEE International Conference on Fuzzy Systems “FUZZ-IEEE”, Istanbul, Turkey, 2015, 1-5. DOI: https://doi.org/10.1109/FUZZ-IEEE.2015.7337813
• [22] K. Yan, D. Zhang, D. Wu, H. Wei, G. Lu, Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction, IEEE Transactions on Biomedical Engineering 61/11 (2014) 2787-2795. DOI: https://doi.org/10.1109/TBME.2014.2329753
• [23] S. Perveen, M. Shahbaz, A. Guergachi, K. Keshavjee, Performance Analysis of Data Mining Classification Techniques to Predict Diabetes, Procedia Computer Science 82 (2016) 115-121. DOI: https://doi.org/10.1016/j.procs.2016.04.016
• [24] K.M. Orabi, Y.M. Kamal, T.M. Rabah, Early Predictive System for Diabetes Mellitus Disease, in: P. Perner (ed.) Advances in Data Mining. Applications and Theoretical Aspects, Lecture Notes in Computer Science, vol. 9728, Springer, Cham, 2016, 420-427. DOI: https://doi.org/10.1007/978-3-319-41561-1_31
• [25] N. Nai-Arun, R. Moungmai, Comparison of Classifiers for the Risk of Diabetes Prediction, Procedia Computer Science 69 (2015) 132-142. DOI: https://doi.org/10.1016/j.procs.2015.10.014
• [26] A. Sharma, P.C. Gupta, Predicting the Number of Blood Donors through their Age and Blood Group by using Data Mining Tool, International Journal of Communication and Computer Technologies 1/6 (2012) 6-10.
• [27] A.G. Karegowda, M.A. Jayaram, A.S. Manjunath, Cascading K-means Clustering and k Nearest Neighbor Classifier for Categorization of Diabetic Patients, International Journal of Engineering Advanced Technology 1/3 (2012) 147-151.
• [28] M. Hardik, M.I. Hasan, K.P. Patel, Comparative study of Naive Bayes Classifier and kNN for Tuberculosis, International Journal of Computer Applications 1 (2011) 22-26.
• [29] Y. Wang, Z. Wang, A Fast KNN Algorithm for Text Categorization," Proceedings of the International Conference on Machine Learning and Cybernetics, Hong Kong, China, 2007, 3436-3441. DOI: https://doi.org/10.1109/ICMLC.2007.4370742
• [30] Y. Angeline Christobel, P. Sivaprakasam, A New Class wise k Nearest Neighbor (CkNN) Method for the Classification of Diabetes Dataset, International Journal of Engineering and Advanced Technology 2/3 (2013) 396-400.
• [31] C. Estébanez, R. Aler, M. Valls, Genetic Programming Base Data Projections for Classification Tasks, International Journal of Computer and Information Engineering 1/7 (2007) 2195-2200. DOI: https://doi.org/10.5281/zenodo.1084684
• [32] J. Han, J.C. Rodriguez, M. Beheshti, Diabetes Data Analysis and Prediction Model Discovery Using Rapid Miner, Proceedings of the 2nd International Conference on Future Generation Communication and Networking, Hainan, China, 2008, 96-99. DOI: https://doi.org/10.1109/FGCN.2008.226

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).