Prognosis using Deep Learning in CoViD-19 patients
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Abstract
Prognostics study the prediction of an event before it happens, to enable efficient critical decision
making. Over the past few years, it has gained a lot of research attention in many fields, i.e.
manufacture, economics, and medicine. Particularly in medicine, prognostics are very useful for
front line physicians to predict how a disease may affect a patient and react accordingly to save as
many lives as possible. One clear example is the recently discovered Coronavirus Disease 2019
(CoViD-19).
Because of its novelty, not nearly enough is known about the virus’ behaviour and Key Performance
Indicators (KPIs) to asses a mortality prediction. However, using a lot of complex and
expensive medical biomarkers could be impossible for many low budget hospitals. This motivates
the development of a prediction model that not only maximizes performance, but does so using the
least amount of biomarkers possible. For mortality risk prediction, falsely assuming that a patient
has a low mortality risk is far more critical than the opposite. Therefore, false negative predictions
should be prioritized over false positive ones.
This research project proposes a CoViD-19 mortality risk calculator based on a Deep Learning
model trained on a data set provided by the HM Hospitales from Madrid, Spain. A pre-processing
strategy for unbalanced classes and feature selection is proposed. Benefit of using over-sampling
and imputation techniques is evaluated. Also, an imputation method based on the K-Nearest Neighbor
(KNN) algorithm for biomarker data is is proposed and its efficiency is evaluated. Results are
compared against a Random Forest (RF) model while showing the trade-off between feature input
space and the number of samples available. Results on the MPCD score show the proposed
DL outperforms the proposed RF on every data set when evaluating even with an over-sampling
technique. Finally, the proposed KNN method proves beneficial for data imputation, improving the
model’s Recall score from 0:87 to 0:90.