Prediction of pediatric HIV/AIDS survival in Nigeria using Naïve Bayes' Approach.
Data 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 predi...
| Publicado en: | International Journal of Child Health & Human Development Vol. 10; no. 2; pp. 131 - 143 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Nova Science Publishers, Inc.
2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124883154&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124883154 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19395965 903J jtl: International Journal of Child Health & Human Development issn: 19395965 maglogo: N pubinfo: dt: 2017 vid: 10 iid: 2 pid: 1040 pub: Nova Science Publishers, Inc. place: Hauppauge, New York artinfo: ui: 124883154 124883154 124883154 124883154 ppf: 131 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Prediction of pediatric HIV/AIDS survival in Nigeria using Naïve Bayes' Approach. aug: au: Idowu, Peter A. Aladekomo, Theophlus A. Agbelusi, Olutola Alaba, Olumuyiwa B. Balogun, Jeremiah A. affil: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria sug: subj: Opportunistic Infections Acquired Immunodeficiency Syndrome Viral Load AIDS Patients Nigeria Human ROC Curve ab: Data 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 nonsurvived cases. Model validation was performed by comparing the model results with that of historical data from two (2) selected tertiary institutions in Nigeria. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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