Clustering as an Artificial Intelligence Technique in Drug Resistance of HIV/AIDS Patients: Case Study Botswana

Rajeswari Chandrasekaran and Audrey Masizana


HIV/AIDS, Drug Resistance, CD Count, Data Mining


HIV/AIDS is a global pandemic that has affected national economies and devastated families. In Botswana, the HIV/AIDS situation is pronounced. The HIV/AIDS infection rate in the country is one of the highest in the world. Annually, Botswana spends a significant amount of its GDP on HIV/AIDS related challenges and initiatives. One such initiative is the Messiah ARV program, which provides free HIV/AIDS treatment for every citizen. While it is being credited as one of the first such initiatives in the world, the program is however faced with challenges. Key among them is the issue of drug resistance. Drug resistance access when existing HIV drugs—are not effective and do not stop the virus from multiplying. These kinds of characteristics are known as drug resistance. When HIV isn't fully controlled by ARV drugs, the virus makes copies of itself at a very rapid rate. Because this replication is occurring so fast, HIV often makes mistakes in the copies. If these “mistaken copies” are able to reproduce themselves, they are called mutations—which creates new forms of the virus. This concept paper presents an ongoing research which aims to investigate the challenge of drug resistance among HIV/AIDS patients in Botswana. The research will employ data mining techniques to monitor the CD Count, as it affects a patient HIV aids cell mutation over time. This will improve the doctor’s decision in recommending the right therapy for patients at any particular given time. The research will confirm that data mining techniques have been used successfully in education and health sectors to assist in the detection of patterns in data, and prediction of the best approaches in addressing challenges. As such, it is seen as a useful tool to apply to this problem.

Important Links:

Go Back