A Study of the Applications of Artificial Intelligence and Machine Learning in Healthcare

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Published: Mar 1, 2020
Keywords:
artificial intelligence, machine learning, healthcare, diabetes care, outcomes, treatment times, data availability, India, medical industry, EMRs

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Authors

Anuja Singh

Research Scholar, Shri Krishna University, Chhatarpur M.P.

Dr. Keshav Samrat Modi

Assistant Professor, Shri Krishna University, Chhatarpur M.P.

Abstract

AIML (artificial intelligencemachine learning) are reshaping many facets of our life, includinghealthcare. The application of AI and ML in diabetes care has the potential to dramatically improveoutcomes while decreasing treatment times. The high number of diabetics in India presents specialdifficulties in terms of data availability, but it also presents an extraordinary opportunity. India might takethe medical industry by storm with the help of EMRs and establish itself as a global powerhouse. Artificialintelligence and machine learning might provide insight into our problems and inspire creative, tailor-madesolutions.

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Issue

Vol. 17 No. 1 (2020): Journal of Advances in Science and Technology (JAST)

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References

  1. Duygu, ç., and Esin, D. (2011). An automatic diabetes diagnosis system based on LDA- wavelet support vector machine classifier. Expert Syst. Appl. 38, 8311–8315.
  2. Sahoo, A. J., & Kumar, Y. (2014). Seminal quality prediction using data mining methods. Technology and Health Care, 22(4), 531-545.
  3. Gambhir, S., Malik, S. K., & Kumar, Y. (2016). Role of soft computing approaches in healthcare domain: a mini review. Journal of medical systems, 40(12), 287.
  4. Ozcift, A., and Gulten, A. (2011). Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput. Methods Programs Biomed. 104, 443–451. doi: 10.1016/j.cmpb.2011.03.018.
  5. Gambhir, S., Malik, S. K., & Kumar, Y. (2017). PSO-ANN based diagnostic model for the early detection of dengue disease. New Horizons in Translational Medicine, 4(1-4),1-8.
  6. Kumar, Y., Yadav, G., Singh, P. K., & Arora, P. (2019). A PHR-Based System for Monitoring Diabetes in Mobile Environment. In Mobile Solutions and Their Usefulness in Everyday Life (pp. 129-144). Springer, Cham.
  7. Gambhir, S., Malik, S. K., & Kumar, Y. (2018). The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches. International Journal of Healthcare Information Systems and Informatics (IJHISI), 13(3), 1-19.
  8. Lin, Y., Qin, H., Chen, R., Liu, Q., Liu, H., & Dong, S. (2019). A comprehensive clinical diagnostic score system for prediction of coronary artery spasm in patients with acute chest pain. IJC Heart & Vasculature, 22, 205-209.
  9. Sabra, S., Malik, K. M., Afzal, M., Sabeeh, V., & Charaf Eddine, A. (2020). A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism. Expert Systems, 37(1), e12388.
  10. Acharjya, D. P. (2020). A Hybrid Scheme for Heart Disease Diagnosis Using RoughSet and Cuckoo Search Technique. Journal of Medical Systems, 44(1), 27.
  11. Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P.(2019). A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992.
  12. Narayan, S., & Sathiyamoorthy, E. (2019). A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases. Neural Computing and Applications, 31(1), 93-102.
  13. Bucholc, M., Ding, X., Wang, H., Glass, D. H., Wang, H., Prasad, G., ... & Finn, D. P. (2019). A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert systems with applications, 130, 157-171.
  14. Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 12(1), 13-20.
  15. Kannadasan, K., Edla, D. R., & Kuppili, V. (2019). Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clinical Epidemiology and Global Health, 7(4), 530-535.