Prediction of HIV Status from Demographic Data Using Neural Networks

Leke-Betechuoh, Brain; Marwala, Tshilidzi; Tim, Taryn; Lagazio, Monica

doi:10.1109/icsmc.2006.385212

Cited by 14 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kay et al [19] used Logistic Regression to find out the health related quality of life(HRQoL) for Irritable Bowel Syndrome patients and found that Psychological morbidity, marital status and employment status were associated with HRQoL. Brain et al [20] experimented by Artificial Neural Network, Multilayer Perceptron to conclude that HIV status of a person based could be predicted based on demographic data. Altikardes et al [21] studied many classifiers like Decision trees, naive Bayes, support vector machines, voted perceptron, multi-layer perceptron, logistic regression etc.…”

Section: Literature Reviewmentioning

confidence: 99%

A methodology for customizing clinical tests for esophageal cancer based on patient preferences

Roy

Bhattacharya

Guin

2019

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

A methodology for customizing clinical tests for esophageal cancer based on patient preferences

Roy

Bhattacharya

Guin

2019

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

“…A study conducted by Brain et al showed that Neural networks were used as pattern recognition tools in data mining to classify HIV status of individuals based on demographic and socio-economic characteristics [27]. The data under that study consists of seroprevalence survey information and contains variables such as age, education, location, race, parity and gravidity.…”

Section: Application Of Data Mining In Hiv Testingmentioning

confidence: 99%

“…The design of classifiers involves the assessment of classification performance, and an accuracy of 84.24% was obtained in that design. This implies that the HIV status of an individual can be predicted using demographic data with 84.24% accuracy [27].…”

Section: Application Of Data Mining In Hiv Testingmentioning

confidence: 99%

Comparing Data Mining Techniques in HIV Testing Prediction

Hailu¹

2015

IIM

View full text Add to dashboard Cite

Introduction: 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, logistic regression) were used to build the model that predicts whether an individual was being tested for HIV among adults in Ethiopia using EDHS 2011. The final experimentation results indicated that the decision tree (random tree algorithm) performed the best with accuracy of 96%, the decision tree induction method (J48) came out to be the second best with a classification accuracy of 79%, followed by neural network (78%). Logistic regression has also achieved the least classification accuracy of 74%. Objectives: The objective of this study is to compare the prediction power of the different data mining techniques used to develop the HIV testing prediction model. Methods: 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. Data preprocessing was performed and missing values for the categorical variable were replaced by the modal value of the variable. Different data mining techniques were used to build the predictive model. Results: 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 modal age of 15-19 years old. Among the study participants, 17,719 (58%) have 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 behaviour attributes, knowledge about where to test for HIV and knowledge on family planning through mass media were found to be predictors for HIV testing. Conclusion and Recommendation: 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 has clinical, community and public health importance at all levels. It is vital to apply different data mining techniques for 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 more T. G. Hailu 154 on the application of data mining techniques in healthcare industry or else in related and similar settings for the future.

show abstract

“…The total of 128 medical dataset having many attributes with Boolean value in addition to age and sex. Researchers suggest that compared to people infected with HCV only, co-infected persons had twice the risk for cirrhosis and approximately six times the risk for liver failure [4]. Basically these infections doesn't show any symptoms in majority of the cases, when they first acquire.…”

Section: Data Setmentioning

confidence: 99%