Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV?
Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset.
Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions.
Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.