With my research project, I aim to investigate whether the need for accurate prediction of both Coronary Heart Disease (CHD) and Hospital Readmission can be addressed by employing a semi-supervised machine learning approach for simultaneous CHD Binary Classification and Hospital Readmission Prediction and Quantification.
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The end result of this project would allow a patient to enter a set of clinical data pertaining to CHD Risk (Cholesterol, Diabetes, Smoking) and use the semi-supervised learning technique to generate the patients' clinical data for Hospital Readmission (and vice versa)
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From there, the model trains on both the inputted clinical data and the generated data to predict whether a patient is at risk of CHD in the next ten years and the time in days of their hospital readmission.