TY - GEN
T1 - Logistic regression and Bayesian approaches in modeling acceptance of Male circumcision in Pune, India
AU - Yoo, C.
AU - Saxena, A.
AU - Krupp, K.
AU - Kulkarni, V.
AU - Kulkarni, S.
AU - Klausner, J. D.
AU - Devieux, J.
AU - Madhivanan, P.
N1 - Publisher Copyright:
© International Congress on Modelling and Simulation, MODSIM 2013.All right reserved.
PY - 2013
Y1 - 2013
N2 - Discernment analyses in survey data are being developed to help researchers better understand intentions of surveyed subjects. These models can aid in successful decision-making by allowing calculation of the likelihood of a particular outcome based on subject's known characteristics. There are many modern discernment analyses which have been used to develop predictive models in many different scientific disciplines areas. Predictive models are used in a variety of public health and medical domains. These models are constructed from observed cases, which are typically collected from various studies. The data can be preprocessed and serve as data to build statistical and machine learning models. The most frequently used discernment analysis in epidemiological datasets with binary outcomes is logistic regression. However, modern discernment Bayesian methods - i.e., Naïve Bayes Classifier and Bayesian networks - have shown promising results, especially with datasets that have a large number of independent variables (>30). A study was conducted to review and compare these models, elucidate the advantages and disadvantages of each, and provide criteria for model selection. The two models were used for estimation of acceptance of medical male circumcision among a sample of 457 males in Pune, India on the basis of their answers to a survey that included questions on sociodemographics, HIV prevention knowledge, high-risk behaviors, and other characteristics. Although the models demonstrated similar performance, the Bayesian methods performed better especially in predicting negative cases, i.e., subjects who did not want to undergo medical male circumcision in cross validation evaluations. Since there were less negative cases in the dataset, this indicates with smaller sample size, Bayesian methods perform better than logistic regression. Identifying models' unique characteristics - strengths as well as limitations - may help improve decision-making.
AB - Discernment analyses in survey data are being developed to help researchers better understand intentions of surveyed subjects. These models can aid in successful decision-making by allowing calculation of the likelihood of a particular outcome based on subject's known characteristics. There are many modern discernment analyses which have been used to develop predictive models in many different scientific disciplines areas. Predictive models are used in a variety of public health and medical domains. These models are constructed from observed cases, which are typically collected from various studies. The data can be preprocessed and serve as data to build statistical and machine learning models. The most frequently used discernment analysis in epidemiological datasets with binary outcomes is logistic regression. However, modern discernment Bayesian methods - i.e., Naïve Bayes Classifier and Bayesian networks - have shown promising results, especially with datasets that have a large number of independent variables (>30). A study was conducted to review and compare these models, elucidate the advantages and disadvantages of each, and provide criteria for model selection. The two models were used for estimation of acceptance of medical male circumcision among a sample of 457 males in Pune, India on the basis of their answers to a survey that included questions on sociodemographics, HIV prevention knowledge, high-risk behaviors, and other characteristics. Although the models demonstrated similar performance, the Bayesian methods performed better especially in predicting negative cases, i.e., subjects who did not want to undergo medical male circumcision in cross validation evaluations. Since there were less negative cases in the dataset, this indicates with smaller sample size, Bayesian methods perform better than logistic regression. Identifying models' unique characteristics - strengths as well as limitations - may help improve decision-making.
KW - Bayesian networks
KW - Discernment analyses
KW - Logistic Regression
UR - https://www.scopus.com/pages/publications/85080956126
UR - https://www.scopus.com/pages/publications/85080956126#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85080956126
T3 - Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013
SP - 2023
EP - 2028
BT - Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013
A2 - Piantadosi, Julia
A2 - Anderssen, Robert
A2 - Boland, John
PB - Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
T2 - 20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 - Held jointly with the 22nd National Conference of the Australian Society for Operations Research, ASOR 2013 and the DSTO led Defence Operations Research Symposium, DORS 2013
Y2 - 1 December 2013 through 6 December 2013
ER -