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The Journal of Computer Science and its Applications Vol. 25, No 1, June, 2018 THE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONS Vol. 25, No 1, June, 2018 PREDICTION OF PEDIATRIC HIV/AIDS SURVIVAL IN NIGERIA USING NAÏVE BAYES’ APPROACH P.A. Idowu1, T. A. Aladekomo2, O. Agbelusi3, and C. O. Akanbi4 1,2 Obafemi Awolowo University, Ile-Ife, Nigeria, 3 Rufus Giwa Polytechnic, Owo, Nigeria 4 Osun State University, Nigeria [email protected] , [email protected] ABSTRACT Epidemic diseases have highly destructive effects around the world and these diseases have affected both developed and developing nations. Disease epidemics are common in developing nations especially in Sub Saharan Africa in which Human Immunodeficiency Virus /Acquired Immunodeficiency Disease Syndrome (HIV/AIDS) is the most serious of all. This paper presents a prediction of pediatric HIV/AIDS survival in Nigeria. Data are collected from 216 pediatric HIV/AIDS patients who were receiving antiretroviral drug treatment in Nigeria was used to develop a predictive model for HIV/AIDS survival based on identified variables. Interviews were conducted with the virologists and pediatricians to identify the variables predictive for HIV/AIDS survival in pediatric patients. 10-fold cross validation method was used in performing the stratification of the datasets collected into training and testing datasets following data preprocessing of the collected datasets. The model was formulated using the naïve Bayes’ classifier – a supervised machine learning algorithm based on Bayes’ theory of conditional probability and simulated on the Waikato Environment for Knowledge Analysis [WEKA] using the identified variables, namely: CD4 count, viral load, opportunistic infection and the nutrition status of the pediatric patients involved in the study. The results showed 81.02% accuracy in the performance of the naïve Bayes’ classifier used in developing the predictive model for HIV/AIDS survival in pediatric patients. In addition, the area under the receiver operating characteristics [ROC] curve had a value of 0.933 which showed how well the developed predictive model was able to discriminate between survived and non-survived cases. Model validation was performed by comparing the model results with that of historical data from two (2) selected tertiary institutions in Nigeria. Keywords: HIV/AIDS survival, naïve Bayes’ classifier, predictive model, pediatrics, machine learning ________________________________________________________________________________ _______ 1.0 Introduction Virus /Acquired Immunodeficiency Disease Epidemic diseases have highly destructive Syndrome (HIV/AIDS) is the most serious of effects around the world and these diseases all [1]. HIV is one of the world’s most serious have affected both developed and developing health and development challenges [2]. It is a nations. Disease epidemics are common in type of virus called a retrovirus which infects developing nations especially in Sub Saharan humans when it comes in contact with tissues Africa in which Human Immunodeficiency such as those that line the vagina, anal area, 1 Prediction of Pediatric HIV/AIDs Survival in Nigeria P.A. Idowu, T. A. Aladekomo, O. Agbelusi, and C. O. Akanbi mouth, eyes or through a break in the skin Antiretroviral therapy is the mechanism of [3], while Acquired Immunodeficiency treating retroviral infections with drugs. The Syndrome (AIDS) is the advanced stage of drugs do not kill the virus but they slow down the retroviral infection that swept through the growth of the virus [14]. HAART refers sub-Saharan Africa with venom [4-5]. to the use of combinations of various Globally, HIV continues to be a very antiretroviral drugs with different serious health issue facing the world [6]. mechanisms of action to treat HIV. About 34 million (31.4 million–35.9 million) The epidemic of HIV/AIDS affects people were living with HIV at the end of two classes of people: the paediatric and the 2011 and an estimated0.8% of adults aged 15- non-paediatric individual. The non-paediatric 49 years worldwide are living with the virus, patients are patients above 15 years of age although the burden of the epidemic while the paediatric patients who form the continues to vary considerably between main target of this research are patients countries and regions [6]. In Sub-Saharan whose age is less than 15 years [15]. There Africa, roughly 25 million people were living are four distinct stages of HIV infection with HIV in 2012, accounting for nearly 70 which includes: the primary HIV infection percent of the global total. The epidemic has stage or clinical stage 1 which involves had widespread social and economic asymptomatic and acute retroviral syndrome ; consequences, not only in the health sector clinically asymptomatic stage or clinical stage but also in education, industry and the wider 2 which involves moderate and unexplained economy [8-9]. The epidemic has had a heavy weight loss [<10% of presumed or measured impact on education, school attendance drops body weight; symptomatic stage or clinical as children become sick or return home to stage 3 is also a conditions where a look after affected family members[10]. presumptive diagnosis could be made on the Moreover, Sub-Saharan Africa remains the basis of clinical signs or simple investigations most severely affected, with nearly 1 in every like unexplained chronic diarrhea for longer 20 adults (4.9%) living with HIV; accounting than one month, unexplained persistent fever for 69% of the people living with HIV [intermittent or constant for longer than one worldwide. Although, the regional prevalence month] and progression or Clinical stage 4 of HIV infection is nearly 25 times higher in that is a condition where a presumptive sub-Saharan Africa than in Asia, almost 5 diagnosis can be made on the basis of clinical million people are living with HIV in South, signs or simple investigations like HIV South-East and East Asia combined and sub- wasting syndrome, pneumocystis pneumonia, Saharan Africa region is the most heavily recurrent severe or radiological bacterial affected region follow by the Caribbean, pneumonia etc. Patient at this stage is in a Eastern Europe and Central Asia, where 1.0% condition where confirmatory diagnostic of adults were living with HIV as at 2011 [2]. testing is necessary [16-17]. Nigeria is the most populous nation in There are different modes of Africa with an estimated population of over transmission of this virus one of which is 160 million people. Government reports mother-to-child transmission. Here, about claim that over 300,000 Nigerians die yearly nine out of ten children exposed are infected of complications arising from AIDS. Nigeria with HIV during pregnancy, labour, delivery has the highest HIV populations in Africa or while breastfeeding [18]. Without with 5.7 million infected people. It is treatment, 15%-30% of babies born to HIV estimated that over 200,000 people die yearly positive women are infected with the virus in Nigeria as a result of HIV/AIDS [11]. At during pregnancy and delivery and a further present, there is no cure for HIV but it is 5%-20% are also infected through being managed with antiretroviral drugs breastfeeding [19]. In high-income countries, [ARV]. There is optimal combination of preventive measures are undertaken to ensure ARV which is known as Highly Active that the transmission of HIV from mother-to- Antiretroviral drug [HAART] [12-13]. child is relatively rare and in cases where it 2 The Journal of Computer Science and its Applications Vol. 25, No 1, June, 2018 occurs, a range of treatment options are unlike regression which allocates a set of undertaken so that the child can survive into records to a real value. This research is adulthood. Blood transfusion is another route focused at using classification models to in which HIV infection can occur in medical classify pediatric HIV/AIDS patients’ setting [20]. survival as either Survived or not survived. HIV epidemic in Nigeria is one of the incurable deadly diseases and it varies widely 2.0 Related Works by region [21]. The impact of HIV/AIDS is Researchers had worked on the prediction of pervasive and far-reaching, affecting HIV/AID prediction using different types of individuals and communities not only variables like CD4 count, CD8. Some of the psychologically but also economically and researcher and the result of their finding are socially. Families lose their most productive in the following paragraphs. members to this disease, leaving children and [27] developed a model to predict elderly people without means of support [22]. survival of HIV/AIDS using sequential and Despite the state of this deadly disease in standard neural networks. The aim of the Nigeria and most especially among children, study was to produce a model of disease there is no existing model in which survival progression in AIDS using sequential neural of infected patient can be predicted. network and compare the model’s accuracy Therefore, this paper present the development with that of a model constructed using only of survival model among paediatric standard neural networks based on HIV/AIDS patients in South Western Nigeria demographic and socioeconomic variables. and the objectives were to identify survival The strength of the study was that sequential variables for HIV/AIDS paediatric patients in neural networks could discriminate patients the South Western Nigeria and formulate who die and patients who survive more survival predictive models based on variables accurately than the standard neural networks. identified [CD4 count, viral load, nutritional The weakness of the study is that only status and opportunistic infection] from the demographic and socioeconomic variables interview conducted with the virologist and were used, which is not sufficient to predict paediatrician at the study area. survival. CD4 count, viral load, opportunistic Machine learning is a branch of infections and nutritional status would be artificial intelligence that allows computers to enough to predict survival accurately in this learn from past examples of data records [23- paper.