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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Prediction Of Diabetes Using Machine Learning Classification Algorithms

[Full Text]

 

AUTHOR(S)

Naveen Kishore G, V.Rajesh, A.Vamsi Akki Reddy, K.Sumedh, T.Rajesh Sai Reddy

 

KEYWORDS

 

ABSTRACT

Diabetes is a sickness that takes place whilst glucose content in your blood is too excessive. Insulin is a hormone made through the pancreas, facilitates to separate glucose from meals get into your body- cells for power. On this we used category set of rules techniques of the device mastering to are expecting the diabetes. Five machine getting to know algorithms namely SVM, selection Tree, NaiveBayes , Logistic Regression and KNN are used to hit upon diabetes. This may be capable of predict the chance degrees of diabetes and gives the first-class getting to know set of rules with better accuracy comparatively different algorithms.

 

REFERENCES

[1] Yu, W., Liu, T., Valdez, R., Gwinn, M., Khoury, M.J., 2010. Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes. BMC Medical Informatics and Decision Making 10. doi:10.1186/1472-6947-10-16, arXiv:arXiv:1011.1669v3
[2] .N.AdityaSundar, P.PushpaLatha, M.Rama Chandra “Performance Analysis of Classification Data Mining Techniques over Heart Disease Database “, International Journal of Engineering Science and Advanced Technology, Vol 2, Issue 3, p470-478,May-June 2012
[3] H. C. Koh and G. Tan, “Data Mining Application in Healthcare”, Journal of Healthcare Information Management, vol. 19, no. 2,(2005).
[4] Andreas G. K. Janecek ,WilfriedN.Gansterer and Michael A.Demel,”On the Relationship Between Feature Selection and Classification Accuracy”,JMLR: Workshop and Conference Proceedings 4: 90-105
[5] Arora, R., Suman, 2012. Comparative Analysis of Classification Algorithms on Different Datasets using WEKA. International Journal of Computer Applications 54, 21–25. doi:10.5120/8626-2492
[6] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. Morgan Kaufmann, 2011
[7] Dhomse Kanchan B., M.K.M., 2016. Study of Machine Learning Algorithms for Special Disease Prediction using Principal of Component Analysis, in: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication, IEEE. pp. 5–10.
[8] Esposito, F., Malerba, D., Semeraro, G., Kay, J., 1997. A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 476–491. doi:10.1109/34.589207
[9] Aljumah, A.A., Ahamad, M.G., Siddiqui, M.K., 2013. Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University - Computer and Information Sciences 25, 127–136. doi:10.1016/j.jksuci.2012.10.003.
[10] Kayaer K., Tulay, 2003. Medical diagnosis on Pima Indian diabetes using general regression neural networks, in: Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP), pp. 181-184
[11] Han, J., Rodriguez, J.C., Beheshti, M., 2008. Discovering decision tree based diabetes prediction model, in: International Conference on Advanced Software Engineering and Its Applications, Springer. pp. 99–109
[12] Garner, S.R., 1995. Weka: The Waikato Environment for Knowledge Analysis, in: Proceedings of the New Zealand computer science research students conference, Citeseer. pp. 57–64
[13] Bamnote, M.P., G.R., 2014. Design of Classifier for Detection of Diabetes Mellitus Using Genetic Programming. Advances in Intelligent Systems and Computing 1, 763–770. doi:10.1007/978-3-319-11933-5.
[14] Iyer, A., S, J., Sumbaly, R., 2015. Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining & Knowledge Management Process 5, 1–14. doi:10.5121/ijdkp.2015.5101, arXiv:1502.03774.
[15] Pradhan, P.M.A., Bamnote, G.R., Tribhuvan, V., Jadhav, K., Chabukswar, V., Dhobale, V., 2012. A Genetic Programming Approach for Detection of Diabetes. International Journal Of Computational Engineering Research 2, 91–94
[16] Perveen, S., Shahbaz, M., Guergachi, A., Keshavjee, K., 2016. Performance Analysis of Data Mining Classification Techniques to Predict Diabetes. Procedia Computer Science 82, 115–121. doi:10.1016/j.procs.2016.04.016
[17] Kumari, V.A., Chitra, R., 2013. Classification Of Diabetes Disease Using Support Vector Machine. International Journal of Engineering Research and Applications (IJERA) www.ijera.com 3, 1797–1801.
[18] Nai-Arun, N., Moungmai, R., 2015. Comparison of Classifiers for the Risk of Diabetes Prediction. Procedia Computer Science 69, 132–142. doi:10.1016/j.procs.2015.10.014
[19] Orabi, K.M., Kamal, Y.M., Rabah, T.M., 2016. Early Predictive System for Diabetes Mellitus Disease, in: Industrial Conference on Data Mining, Springer. Springer. pp. 420–427
[20] Priyam, A., Gupta, R., Rathee, A., Srivastava, S., 2013. Comparative Analysis of Decision Tree Classification Algorithms. International Journal of Current Engineering and Technology Vol.3, 334–337. doi:JUNE 2013, arXiv:ISSN 2277 – 4106
[21] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., Chouvarda, I., 2017. Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal 15, 104–116. doi:10.1016/j.csbj.2016.12.005.
[22] Ray, S., 2017. 6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python)
[23] Sisodia, D., Shrivastava, S.K., Jain, R.C., 2010. ISVM for face recognition. Proceedings - 2010 International Conference on Computational Intelligence and Communication Networks, CICN 2010 , 554–559doi:10.1109/CICN.2010.109.
[24] .Bhargavi V.R., Senapati R.K., Curvelet fusion enhacement based evaluation of diabetic retinopathy by the identification of exudates in optic color fundus images ,2016, Biomedical Engineering - Applications, Basis and Communications, Vol: 28, Issue: 6, ISSN 10162372
[25] Bhargavi V R., Senapati R.K., Swain G., Prasad P.M.K., Computerized diabetic patient’s fundus image screening for lesion regions detection and grading ,2016, Biomedical Research (India), Vol: 2016, Issue: Special Issue 1, pp: S443 - S449, ISSN 0970938X
[26] Kumar K.V.V., Kishore P.V.V., Indian classical dance mudra classification using HOG features and SVM Classifier,2017 International Journal of Electrical and Computer Engineering, Vol:7, issue:5, pp: 2537-2546, DOI: 10.11591/ijece. v7i5. pp2537-2546, ISSN: 20888708
[27] . Mirza S.S., Rahman M.Z.U., Efficient adaptive filtering techniques for thoracic electrical bio-
[28] impedance analysis in health care systems,2017 Journal of Medical Imaging and Health Informatics, Vol:7, issue:6, pp: 1126-1138, DOI: 10.1166/jmihi.2017.2211, ISSN: 21567018
[29] . .Rao G.A., Syamala K., Kishore P.V.V., Sastry A.S.C.S. .," Deep convolutional neural networks for sign language recognition “, 2018, International Journal of Engineering and Technology(UAE) ,Vol: 7 ,Issue: 1.5 Special Issue 5 ,pp: 62 to:: 70 ,DOI: ,ISSN: 2227524X
[30] . .Reddy S.S., Suman M., Prakash K.N. .," Micro aneurysms detection using artificial neural networks “, 2018, Lecture Notes in Electrical Engineering ,Vol: 471 ,Issue: ,pp: 273 to:: 282 ,DOI: 10.1007/978-981-10-7329-8_28 ,ISSN: 18761100 9.78981E+12
[31] 209. Putluri S., Ur Rahman M.Z., Fathima S.Y. .," Cloud-based adaptive exon prediction for DNA analysis “, 2018, Lecture Notes in Electrical Engineering ,Vol: 434 ,Issue: ,pp: 409 to:: 417 ,DOI: 10.1007/978-981-10-4280-5_43 ,ISSN: 18761100 9.78981E+12
[32] Rajesh V., Saikumar K., Ahammad S.K.H., "A telemedicine technology for cardiovascular patients diagnosis feature using knn-mpm algorithm", Journal of International Pharmaceutical Res
[33] earch, ISSN:16740440, Vol No:46, 2019, pp:72 - 77.