A machine learning approach for mortality prediction only using non-invasive parameters.

Medical & Biological Engineering & Computing
Guang ZhangFeng Chen

Abstract

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between th...Continue Reading

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Citations

Oct 13, 2021·Scientific Reports·Yazeed ZoabiNoam Shomron

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MEWS
Python
XGBoost
OASIS
LightGBM
MATLAB
pgAdmin

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