A Comparison of Logistic Regression, Modified Logistic Regression and Naïve Bayes Models for Classifying HIV Viral load Suppression: The Case of Zombo District in Uganda

Purpose:To enhance the performance of the logistic regression by integrating step-wise procedures and henceforth compare and evaluate its performance with the Logistic regression and Naïve Bayes in classifying HIV viral load suppression (VLS).

Methods:Models for classifying VLS were built using Logistic regression, modified logistic regression and Naïve Bayes classifiers. Accuracy, balanced accuracy and the area under the receiver operating characteristics curve (AUC) were the key performance metrics used to evaluate the generalizability of the various classifiers.

Results:The modified logistic regression model trained on fewer predictor attributes achieved an accuracy of 84.9%, a balanced accuracy of 83.8% and an AUC of 92.6%. The traditional logistic regression model trained on a full set of predictor attributes achieved an accuracy of 84.9%, a balanced accuracy of 83.6% and an AUC of 92.5% whereas the naïve Bayes model achieved an accuracy of 81.6%, a balanced accuracy of 80.5% and AUC of 89.4%.

Conclusion:The modified logistic regression model outperformed the traditional logistic regression and naïve Bayes models on account of recording higher balanced accuracy and AUC values of 83.8% and 92.6% respectively albeit with fewer predictor attributes. Hence integrating step-wise regression procedures in the traditional logistic regression model can enhance its classification performance leading to better predictions.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.