The week's pick
Reseach Article
Knowledge Discovery in Medical Database using Machine Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 35 |
| Year of Publication: 2019 |
| Authors: Adebola K. Ojo, Ahmed B. Olanrewaju |
10.5120/ijca2019919221
|
Adebola K. Ojo, Ahmed B. Olanrewaju . Knowledge Discovery in Medical Database using Machine Learning Techniques. International Journal of Computer Applications. 178, 35 ( Jul 2019), 14-21. DOI=10.5120/ijca2019919221
Abstract
In this study, an attempt was made using machine learning techniques to discover knowledge that will assist policy makers in taking decisions that will ensure that the sustainable development goals on Health is met. Agglomerative Hierarchical clustering was used to cluster the states by personnel information (number of doctors, community health workers, nurses and midwives), this was visualized using a dendrogram. The Exploratory analysis revealed that it is only community health workers that are well distributed in all the states, the North West states have the least number of hospitals offering ante-natal services. Random Forest model was used to generate a feature importance to determine the important attributes that determined the availability of maternal health delivery services in a hospital, an important discovery was the fact that the availability of doctors does not in any way determine the availability of maternal health delivery services but rather community health workers, nurses and midwives are the major determinants. Random Forest algorithm was also used to classify hospitals offering maternal health delivery services and the result compared with Logistic Regression, Bagging and Boosting. The evaluation metrics used were accuracy, precision and recall. For accuracy and precision, Random Forest performed best while for recall it performed poorly compared to all the other algorithms.
References
- F. Osinupebi 2018, "Energy access; Energy demand". http://csd.columbia.edu/2014/03/10/the-nigeria-mdg-information-system-nmis-takes-open-data-further/
- Nada Lavrac, Aleksander Pur, Marko Bohanec and Bojan Cestnik 2007. "Data mining and visualization for decision support and modeling of public health-care resources" Journal of Biomedical Informatics, 40 (4): 438–447.
- Jiawei Han 2006, Data Mining: Concepts and Techniques, San Francisco, Calif.: Morgan Kaufmann; Oxford: Elsevier Science [distributor], 2nd ed.
- Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, 1996, From Data Mining to Knowledge Discovery in Databases, American Association for Artificial Intelligence, AI Magazine.
- K. J. Cios, W. Pedrycz, R. W. Swiniarski, and L. A. Kurgan 2007, “Data mining: A knowledge discovery approach,” Springer, New York.
- Osmar R. Zaiane, Andrew Foss, Chi-Hoon Lee and Weinan Wang 2002, "On Data Clustering Analysis: Scalability, Constraints, and Validation". University of Alberta, Edmonton, Alberta, Canada. Chapter from book Advances in Knowledge Discovery and Data Mining, 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002, Proceedings (pp.28-39)
- Ahmed Hammad and Simaan AbouRizk, 2014, "Knowledge Discovery in Data: A Case Study," Journal of Computer and Communications. 2(5): 1-27.
- Jiawei Han, Micheline Kamber and Jian Pei 2011, Data Mining: Concepts and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers, 2011. Elsevier ISBN 978-0123814791.
- Gordon S. Linoff, Michael J. A. Berry 2011, Data mining techniques: for marketing, sales, and customer relationship management, 3rd Edition John Wiley & Sons, ISBN: 978-1-118-08745-9.
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