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Mulaudzi, T. B. Bere, A. Ndou, Sedzani Emanuel 2024 2025-02-18T13:10:51Z 2025-02-18T13:10:51Z 2024-09-06 Ndou, S.E. 2024. Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis. . . https://univendspace.univen.ac.za/handle/11602/2800 Ndou SE. Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis. []. , 2024 [cited yyyy month dd]. Available from: Ndou, S. E. (2024). <i>Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis</i>. (). . Retrieved from Ndou, Sedzani Emanuel. <i>"Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis."</i> ., , 2024. TY - Dissertation AU - Ndou, Sedzani Emanuel AB - While statistical models have been traditionally utilized, there is a growing interest in exploring the potential of machine learning techniques. Existing literature shows varying results on their performance which is based on the dateset employed. This study will conduct a comparative evaluation of the predictive accuracy of both statistical and machine learning models for continuous survival analysis utilizing two distinct datasets: time to first alcohol intake and North Carolina recidivism data. LassoCV was used to select variables for both datasets by encouraging limited coefficient estimates. Kaplan-Meier survival curves were utilized to compare the survival distributions among groups of variables incorporated in the model, alongside the logrank test. The proposed methods include the Cox Proportional Hazards, Lasso-regularized Cox, Survival Trees, Random Survival Forest, and Neural Networks. Model performance was evaluated using Integrated Brier score (IBS), Area Under the Curve and Concordance index. Our findings shows consistent dominance of Neural Network (NN) and Random Survival Forest (RSF) models across multiple metrics for both datasets. Specifically, Neural Network demonstrates remarkable performance, closely followed by RSF, CoxPH and CoxLasso models with slightly lower performance, and Survival Tree (ST) consistently lags behind. This study can contribute to advancing knowledge and provides practical guidance for improving survival in recidivism and alcohol intake. DA - 2024-09-06 DB - ResearchSpace DP - Univen KW - Survival analysis KW - Statistical models KW - Machine Learning models KW - Integrated Brier score KW - Concordance Index KW - Area under the curve LK - https://univendspace.univen.ac.za PY - 2024 T1 - Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis TI - Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis UR - ER - M.Sc (e-Science) Department of Mathematical and Computational Sciences While statistical models have been traditionally utilized, there is a growing interest in exploring the potential of machine learning techniques. Existing literature shows varying results on their performance which is based on the dateset employed. This study will conduct a comparative evaluation of the predictive accuracy of both statistical and machine learning models for continuous survival analysis utilizing two distinct datasets: time to first alcohol intake and North Carolina recidivism data. LassoCV was used to select variables for both datasets by encouraging limited coefficient estimates. Kaplan-Meier survival curves were utilized to compare the survival distributions among groups of variables incorporated in the model, alongside the logrank test. The proposed methods include the Cox Proportional Hazards, Lasso-regularized Cox, Survival Trees, Random Survival Forest, and Neural Networks. Model performance was evaluated using Integrated Brier score (IBS), Area Under the Curve and Concordance index. Our findings shows consistent dominance of Neural Network (NN) and Random Survival Forest (RSF) models across multiple metrics for both datasets. Specifically, Neural Network demonstrates remarkable performance, closely followed by RSF, CoxPH and CoxLasso models with slightly lower performance, and Survival Tree (ST) consistently lags behind. This study can contribute to advancing knowledge and provides practical guidance for improving survival in recidivism and alcohol intake. National Research Foundation (NRF) 1 online resource (xiv, 83 leaves) en University of Venda Survival analysis Statistical models Machine Learning models Integrated Brier score Concordance Index Area under the curve UCTD Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis Dissertation open.access