School of Computer Science and Applied Mathematics (ETDs)

Permanent URI for this community

https://hdl.handle.net/10539/38004

Browse

Browsing School of Computer Science and Applied Mathematics (ETDs) by Keyword "AutoPrognosis"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Modelling Cohort Specific Metabolic Syndrome and Cardiovascular Disease Risk using Supervised Machine Learning
    (University of the Witwatersrand, Johannesburg, 2023-08) Ngcayiya, Paulina Genet; Ranchod, Pravesh
    Cardiovascular Disease (CVD) is the leading cause of death worldwide, with Coronary Heart Disease (CHD) being the most common type of CVD. The consequences of the presence of CVD risk factors often manifest as Metabolic Syndrome (MetS). In this study, a dataset from the Framingham Heart Study (FHS) was used to develop two different kinds of CHD risk prediction models. These models were developed using Random Forests (RF) and AutoPrognosis. Performance of the Framingham Risk Score model (AUC-ROC: 0.633) on the FHS dataset was used as the benchmark. The RF model with optimized hyperparameters (AUC-ROC: 0.728) produced the best results. This was by a very small margin to the AutoPrognosis model with an ensemble pipeline (AUC-ROC: 0.714). The performance of RF against AutoPrognosis when predicting the existence of MetS was evaluated using a dataset from the National Health and Nutrition Examination Survey (NHANES). The RF model with optimized hyperparameters (AUC ROC: 0.851) produced the best results. This was by a small margin to the AutoPrognosis model with an ensemble pipeline (AUC-ROC: 0.851). Datasets, varying in size from 100 to 4900, were used to test the performance of RF against AutoPrognosis. The RF model with optimized hyperparameters had the best performance results.