Disease prediction via Bayesian hyperparameter optimization and ensemble learning.
Journal:
BMC research notes
PMID:
32276658
Abstract
OBJECTIVE: Early disease screening and diagnosis are important for improving patient survival. Thus, identifying early predictive features of disease is necessary. This paper presents a comprehensive comparative analysis of different Machine Learning (ML) systems and reports the standard deviation of the results obtained through sampling with replacement. The research emphasises on: (a) to analyze and compare ML strategies used to predict Breast Cancer (BC) and Cardiovascular Disease (CVD) and (b) to use feature importance ranking to identify early high-risk features.