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Diabetic Disease Prediction Using Machine Learning

Aakriti Ashutosh Mishra Harsh Verma Prof. Shripad Desai

Subject area: Science,Engineering and Technology  ·  Area of research: Electrical Engineering

Abstract

Many of the interesting and important applications of machine learning are seen in a medical organization. The notion of machine learning hasswiftly become very appealing to healthcare industries. The predictions and analysis made by the research community for medical dataset support the people by taking proper care and precautions by preventing diseases. Through a set of medical datasets, different methods are used extensively in developing the decision support systems for disease prediction. We also discuss various applications of machine learning in the field of medicine focusing on the prediction of diabetes through machine learning. Diabetes is one of the most increasing diseases in the world and it requires continuous monitoring. To check this, we explore various machine learning algorithms which will help in early prediction of this disease.

Keywords

Diabetes; health care; Random Forest, Xg Boost; machine learning.

References

[1] DRAP: Decision tree and random forest- based classification model to predict diabetes, conference paper, Jan 2019.

[2] Alumax, A.A., Ahamad, M.G., Siddiqui, M.K., 2019. Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University.

[3] Debary Dutta, Debroy Paul, Perthanes Ghosh, "Analyzing Feature Importance’s for Diabetes Prediction using Machine Learning". IEEE, pp 942-928, 2018.

[4] Parveen, S., Shahbaz, M., Guaracha, A., Keshav, K., 2016. Performance Analysis of Datamining Classification Techniques to Predict Diabetes. Procedia Computer Science 82, 115–121. Doi: 10.1016/j.procs.2014.04.016.

[5] International Diabetes Federation. Diabetes Atlas. 5th ed. Brussels, Belgium: IDF Publications. (2011) the Global Burden of Diabetes; pp.7–13.Availablefrom http://www.idf.org/diabetesatlas/news/fifth- edition-release. Accessed 25, May2015.

[6] V. A. Kumari and R. Chitra, “Classification of diabetes disease using support vector machine,” International Journal of Engineering Research and Applications, vol. 3, no. 2, pp. 1797– 1801, 2013.

[7] G. Kare Gowda, M. Jayaram, and A. Manjunath, “Cascading k- means clustering and k-nearest neighbor classifier for categorization of diabetic patients,” International Journal of Engineering and Advanced Technology, vol. 1, no. 3, pp. 147– 151, 2012.

[8] H. C. Koh and G. Tan, ―Data Mining Application in Healthcare‖, Journal of Healthcare Information Management, vol. 19, no. June 2005.

[9] L.J. Muhammad, “Predictive Data Mining Models for Novel Coronavirus (COVID-

[10] Infected Patients’ Recovery”, SN Computer Science, Vol. 1, No. 4, pp. 1-7, 2020.

[11] Sa yank Paul, "Fledgling's Guide to Feature SelectioninPython",Available at https://www.datacamp.com/local rea/instructional exercises/featureless on- python, Accessed at 2021.

How to cite this paper

Aakriti, Ashutosh Mishra, Harsh Verma, Prof. Shripad Desai "Diabetic Disease Prediction Using Machine Learning" Iconic Research And Engineering Journals Volume 6 Issue 1 2022 Page 88-92
Aakriti, Ashutosh Mishra, Harsh Verma, Prof. Shripad Desai "Diabetic Disease Prediction Using Machine Learning" Iconic Research And Engineering Journals, vol. 6, no. 1, Jul. 2022
Aakriti, Ashutosh Mishra, Harsh Verma, Prof. Shripad Desai (2022). Diabetic Disease Prediction Using Machine Learning. Iconic Research And Engineering Journals, 6(1).
Aakriti, Ashutosh Mishra, Harsh Verma, Prof. Shripad Desai "Diabetic Disease Prediction Using Machine Learning" Iconic Research And Engineering Journals, vol. 6, no. 1, Jul. 2022.
@article{1703601,
      author = {Aakriti, Ashutosh Mishra, Harsh Verma, Prof. Shripad Desai},
      title = {Diabetic Disease Prediction Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {1},
      pages = {88-92},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703601.pdf},
      abstract = {Many of the interesting and important applications of machine learning are seen in a medical organization. The notion of machine learning hasswiftly become very appealing to healthcare industries. The predictions and analysis made by the research community for medical dataset support the people by taking proper care and precautions by preventing diseases. Through a set of medical datasets, different methods are used extensively in developing the decision support systems for disease prediction. We also discuss various applications of machine learning in the field of medicine focusing on the prediction of diabetes through machine learning. Diabetes is one of the most increasing diseases in the world and it requires continuous monitoring. To check this, we explore various machine learning algorithms which will help in early prediction of this disease.},
      keywords = {Diabetes; health care; Random Forest, Xg Boost; machine learning.},
      month = {July},
  }