  Electronic Health Records (EHRs) are widely applied in healthcare facilities
nowadays. Due to the inherent heterogeneity, unbalanced, incompleteness, and
high-dimensional nature of EHRs, it is a challenging task to employ machine
learning algorithms to analyse such EHRs for prediction and diagnostics within
the scope of precision medicine. Dimensionality reduction is an efficient data
preprocessing technique for the analysis of high dimensional data that reduces
the number of features while improving the performance of the data analysis,
e.g. classification. In this paper, we propose an efficient curvature-based
feature selection method for supporting more precise diagnosis. The proposed
method is a filter-based feature selection method, which directly utilises the
Menger Curvature for ranking all the attributes in the given data set. We
evaluate the performance of our method against conventional PCA and recent ones
including BPCM, GSAM, WCNN, BLS II, VIBES, 2L-MJFA, RFGA, and VAF. Our method
achieves state-of-the-art performance on four benchmark healthcare data sets
including CCRFDS, BCCDS, BTDS, and DRDDS with impressive 24.73% and 13.93%
improvements respectively on BTDS and CCRFDS, 7.97% improvement on BCCDS, and
3.63% improvement on DRDDS. Our CFS source code is publicly available at
https://github.com/zhemingzuo/CFS.
