Efficacy of Knowledge Mining and Machine Learning Techniques in Healthcare Industry
Knowledge mining is the process of discovering the knowledge from the larger database. As the size of the data is increasing enormously in the healthcare industry knowledge mining techniques are used to extract and mine the dataset to acquire new knowledge. Machine learning is a technique of training the system. In connection with artificial intelligence, statistics, and computer science, it is also known as statistical learning or predictive analytics. In recent years, application of machine learning and knowledge mining methods is been used everywhere in daily life. Healthcare system can cater prime diagnosis data of human healthcare details and reference to the doctors. Historical medical records afford other healthcare providers to access quickly and recognize the patients past and current health status. Chronic obstructive pulmonary disease is becoming one of the causes for leading deaths. An experiment is conducted to predict the presence and severity of the chronic obstructive pulmonary diseases (COPDs) using knowledge mining and machine learning techniques. Logistic and multinominal regression has been implemented to predict the prevalence of the disease using attributes from various sources and structures.
KeywordsChronic obstructive pulmonary disease (COPD) Machine learning Logistic regression Multinomial logistic regression Regression analysis
We would like to express our special thanks of gratitude and deep regards to Dr. Yunus Sheriff pursuing DNB Pulmonology for his classic guidance, appreciated feedback and constant encouragement in understanding COPD and analyze the complications. His valuable suggestions were of immense help to us in getting this work done.
- 1.Unstructured Data: A Big Deal in Big Data. (n.d.). Retrieved from http://www.digitalreasoning.com.
- 2.Sun, J., & Reddy, C. K. (2013). Big Data analytics for healthcare. In SIAM International Conference on Data Mining.Google Scholar
- 3.Liu, W., & Park, E. K. (2014). Big Data as an e-health service. International Conference on Computing, Networking and Communications, 3(6), 982–988.Google Scholar
- 4.Feldman, B., Martin, E. M., & Skotnes, T. (2012). Big Data in healthcare hype and hope. Dr. Bonnie.Google Scholar
- 5.Mukherji, A. (n.d.). Indian healthcare system: Challenges and opportunities. Retrieved from http://tejas.iimb.ac.in/interviews/41.php.
- 6.Kumar, R., & Indrayan, A. (2011). Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatrics, 48, 277–287.Google Scholar
- 7.The Global Burden of Disease. (n.d.). Retrieved December 22, 2011, from www.who.int/healthinfo/global_burden_disease/projections/en/index.html.
- 8.Salvi, S. (2011). In S. K. Jindal (Ed.), COPD: The neglected epidemic: Pulmonary and Critical Care Med (pp. 971–974). Jaypee.Google Scholar
- 9.Adeloye, D., Basquill, C., Papana, A., Chan, K., Rudan, I., & Campbell, H. (2015). An estimate of the prevalence of COPD in Africa. A systematic analysis. COPD: Journal of Chronic Obstructive Pulmonary Disease, 12(1), 71–81.Google Scholar
- 11.Peek, N., Holmes, J. H., & Sun, J. (2014). Technical challenges for Big Data in biomedicine and health: Data sources, infrastructure, and analytics. Yearbook of Medical Informatics, 9(1), 42–47. http://doi.org/10.15265/IY-2014-0018.
- 12.Global Strategy for the Diagnosis. (2014). Management and prevention of chronic obstructive pulmonary disease, Global Initiative for Chronic Obstructive Lung Disease.Google Scholar
- 13.Koppad, S., & Kumar, A. S. (2016). Application of Big Data analytics in healthcare system to predict COPD. In International Conference on Circuit, Power and Computing Technologies [ICCPCT] IEEE, ISBN-16 978-1-5090-1276-3.Google Scholar
- 14.Futrel, K. (2013, October). Structured data: Essential for healthcare analytics & interoperability. MT(ASCP).Google Scholar
- 15.Raghupathi. (2014). Health Information Science and Systems, 2(3). Retrieved from http://www.hissjournal.com/content/2/1/3.