Utilization of Machine Learning in a Responsible Manner in the Healthcare Sector


  • Venkata Koteswara Rao Ballamudi Sr. Software Engineer, High Quartile LLC, Chesterfield, MO 63005, USA


Medical Informatics, Machine Learning, Healthcare, Patient Outcome, Artificial Intelligence


Artificial Intelligence and Machine Learning technologies have improved the ability to forecast and recognize health emergencies, disease populations, and disease status and immune response. Many people still need to be convinced about using ML-based approaches in healthcare, yet their incorporation is increasing regardless of such reservations. Here are brief descriptions and examples of supervised, unsupervised, and reinforcement learning algorithms and machine learning-based methodologies. Second, we discuss how ML is used in healthcare, like X-rays, DNA analysis, EHRs, and MRIs. We also offer solutions to the problems that arise when applying ML to healthcare, such as system privacy and ethical considerations, and point the way toward potential future applications.


Download data is not yet available.


Ibrikci, T., Ustun, D., Kaya, I. E. (2012). Diagnosis of Several Diseases by Using Combined Kernels with Support Vector Machine. Journal of Medical Systems, 36(3), 1831-40. https://doi.org/10.1007/s10916-010-9642-5

Katherine, E., Suneeta, G., Simon, M., Gert, L., John, S., Jacqueline, K. (2014). Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms. Front. Public Health. 2(36). https://doi.org/10.3389/fpubh.2014.00036.

Lal, K. (2015). How Does Cloud Infrastructure Work?. Asia Pacific Journal of Energy and Environment, 2(2), 61-64. https://doi.org/10.18034/apjee.v2i2.697

Omrani, H. (2015). Predicting travel mode of individuals by machine learning. Transportation Research Procedia, 10, 840–849. https://doi.org/10.1016/j.trpro.2015.09.037

Pollettini, J. T., Panico, S. R. G., Daneluzzi, J. C., Tinós, R., Baranauskas, J. A. (2012). Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records. Journal of Medical Systems, 36(6), 3861-74. https://doi.org/10.1007/s10916-012-9859-6

Sweeney, E. M., Vogelstein, J. T., Cuzzocreo, J. L., Calabresi, P. A., Reich, D. S. (2014). A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI. PLoS One, 9(4), e95753. https://doi.org/10.1371/journal.pone.0095753

Thaduri, U. R., Ballamudi, V. K. R., Dekkati, S., & Mandapuram, M. (2016). Making the Cloud Adoption Decisions: Gaining Advantages from Taking an Integrated Approach. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 11–16. https://upright.pub/index.php/ijrstp/article/view/77

Wang, C. (2015). A Modified Machine Learning Method Used in Protein Prediction in Bioinformatics. International Journal Bioautomation, 19(1), 25-36.

Yan, P., Kenji, S., Wang, F., Shen, D. (2013). Machine Learning in Medical Imaging. Machine Vision and Applications, 24(7), 1327-1329. https://doi.org/10.1007/s00138-013-0543-8






Peer-reviewed Article

How to Cite

Ballamudi, V. K. R. (2016). Utilization of Machine Learning in a Responsible Manner in the Healthcare Sector. Malaysian Journal of Medical and Biological Research, 3(2), 117-122. https://mjmbr.my/index.php/mjmbr/article/view/677

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >>