Target-Focused Feature Selection Using Uncertainty Measurements in Healthcare Data

Abstract

Healthcare big data remains under-utilized due to various incompatibility issues between the domains of data analytics and healthcare. The lack of generalizable iterative feature acquisition methods under budget and machine learning models that allow reasoning with a model’s uncertainty are two examples. Meanwhile, a boost to the available data is currently under way with the rapid growth in the Internet of Things applications and personalized healthcare. For the healthcare domain to be able to adopt models that take advantage of this big data, machine learning models should be coupled with more informative, germane feature acquisition methods, consequently adding robustness to the model’s results. We introduce an approach to feature selection that is based on Bayesian learning, allowing us to report the level of uncertainty in the model, combined with false-positive and false-negative rates. In addition, measuring target-specific uncertainty lifts the restriction on feature selection being target agnostic, allowing for feature acquisition based on a target of focus. We show that acquiring features for a specific target is at least as good as deep learning feature selection methods and common linear feature selection approaches for small non-sparse datasets, and surpasses these when faced with real-world data that is larger in scale and sparseness.

Z-Score Normalized Feature Selection and Iterative African Buffalo Optimization for Effective Heart Disease Prediction

“…However, healthcare heart disease big data remains under-utilized due to numerous conflict proceedings between domains of data analytics and healthcare. Feature selection based on Bayesian learning was applied in [14], therefore minimizing false positive and false negative rates considerably.…”

Development and Performance Evaluation of a Novel Bayesian Network Model for the Classification of Heart Disease

“…We believe that not all variables are related to our study. We applied feature selection using Bayesian uncertainty [32,33]. This is a common technique used in machine learning and statistics to select relevant features and eliminate irrelevant ones.…”

A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS)

“…Intentions behind the usage of Bayesian methodology vary significantly between authors and do not necessarily involve expert knowledge. Examples include Dalton (2013), who investigates sparsity priors, and Goldstein et al (2020), who suggest a Bayesian framework to quantify the level of uncertainty in the underlying feature selection model. Other Bayesian approaches for feature selection include Saon and Padmanabhan (2001), and Lyle et al (2020), but these works do not investigate the usage of expert knowledge as prior.…”