Application of Data Mining Techniques on Antiretroviral Therapy (Art) Data: The Case of Adama and Asella Hospitals
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Date
2010-06
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Addis Ababa University
Abstract
Human Immunodeficiency Virus/ Acquired Immunodeficiency Syndrome (HIV/AIDS) is of global as well as national concern today as it affects all people of the world regardless of sex, age, educational status, race and color. When we come to Sub-Saharan African region in general and Ethiopia in particular, the situation is even more worsening and needs special attention. Today more than 1 million people are living with HIV/AIDS in Ethiopia. The country has made a lot of efforts towards preventing and controlling of the disease. As a result, hundreds of thousands of people come to health facilities to get Counseling and testing services through Voluntary Counseling and Testing (VCT) and Antiretroviral Therapy (ART) programs.
A lot of demographic and Clinical data is recorded about individuals taking the services. As these data is getting larger and larger, it is highly likely that there will be hidden, implicit and non trivial knowledge within the data, which might not be obtained by the traditional statistical analysis as well as report and query based database functionalities. There are various evidences that Data Mining (DM) helps the health care system to extract non-trivial and hidden knowledge which exists within the large volume of demographic and clinical data captured during the provision of services and that this knowledge is helpful for health administrators to target resources in the right directions for preventive and controlling activities, and clinicians to give safe and right treatment and saves humans’ lives.
Therefore; the main objective of this research was to see the applicability of data mining techniques on ART data collected at facility level by taking the case of Adama and Asella Hospitals ART databases to identify important patterns related to determinant attributes and their values for Termination/ Continuity behavior of patient on ART care service. Various data preprocessing activities were made to come up with the dataset ready for model building. The researcher selected two DM functionalities (Classification and Association rules mining). Decision tree classification with J48 implementation with eight scenarios was experimented. Thirteen experiments with different parameters were made for association rule mining. Evalution of the models was performed by using for each DM functionality and scenarios used to model the dataset. Analysis of the model was made based on different criteria mainly using confusion matrix, accuracy measures,time of execution and tree complexity for decision tree classification models and number of rules generated, support and confidence for each scenario of the association rule
The research showed encouraging results; that data mining techniques are of high potential in predicting determinant factors/attributes for termination/continuity behavior of ART care by the patients.
Finally hidden patterns (knowledge) were extracted that will provide certain decision support information for concerned bodies, for ART programs intervention. To mention few, the result showed for example that those patients who were on ART stage and whose Functional status is bedridden and the year in which they began the service is before 1999 E.C are at high risk of terminating the ART care. Those patients whose ART stage is on ART, and whose functional Status is Ambulatory, and if they started the service before 1999 and their age is above 18 years then they have high chance to terminate the ART care. The study also showed certain hidden information that young people whose age is less than 18 years; have high chance of staying longer in ART care service. Patients terminate the service in shorter time at Asella hospital than at Adama hospital. Those who are jobless have high chance to stay in the care. The reason (s) for these hidden patterns is left open for future researches works. From comparisons done among the experimentations made, it was learned that those data mining techniques, which were experimented for this research are applicable on the ART dataset of the cases under investigation in general but generalized decision tree with pruning outperformed for classification purpose on the dataset in terms preciseness, providing general insight, Performances and accuracy measures with fair execution time and providing best interpretable patterns. Many association rules were obtained with minimum support of 30% and confidence 50% had provided optimum rules with acceptable patterns.
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Application of Data Mining Techniques