Applications of data mining models in Healthcare Industry

Abstract:

The present work compared the prediction power of the different data mining techniques used to develop the HIV testing prediction model. Four popular data mining algorithms (Decision tree, Naive Bayes, Neural network, and logistic regression) were used to build the model that predicts whether an individual was being tested for HIV among adults using EDHS 2011. Experiment results showed that the decision tree (random tree algorithm) performed best with a classification accuracy of 96%, and the decision tree induction method (J48) performed second best with a classification accuracy of 79%, followed by a neural network (78%). In addition, logistic regression has the lowest classification accuracy of 74%. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes.

The target dataset contained 30,625 study participants. Out of which, 16,515 (54%) participants were women, while the rest, 14,110 (46%) were men. The age of the participants in the dataset ranged from 15 to 59 years old, with a modal age of 15–19 years old. Among the study participants, 17,719 (58%) had never been tested for HIV, while the rest, 12,906 (42%) had been tested. Residence, educational level, wealth index, HIV-related stigma, knowledge related to HIV, region, age group, risky sexual behavior attributes, knowledge about where to test for HIV, and knowledge of family planning through mass media were found to be predictors of HIV testing. The results obtained from this research reveal that data mining is crucial in extracting relevant information for the effective utilization of HIV testing services, which have clinical, community, and public health importance at all levels. It is vital to apply different data mining techniques to the same settings and compare the model performances (based on accuracy, sensitivity, and specificity) with each other. Furthermore, this study would also invite interested researchers to explore further the application of data mining techniques in the healthcare industry or else in related and similar settings in the future.

The full article can be found at: http://dx.doi.org/10.4236/iim.2015.73014

Other related research works can be found at: https://bit.ly/3Bu0Pu9

Keywords

Data Mining, Comparison, Predictive Modeling, HIV Testing