Disease prediction via Bayesian hyperparameter optimization and ensemble learning.

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.

Results

The Bayesian hyperparameter optimization method was more stable than the grid search and random search methods. In a BC diagnosis dataset, the Extreme Gradient Boosting (XGBoost) model had an accuracy of

SUBMITTER: Gao L 

PROVIDER: S-EPMC7146897 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature