A fuzzy transformation approach to enhance active learning for heart disease prediction

Abstract

When providing patient care, healthcare professionals often rely on interpreting laboratory and clinical test results. However, their analysis is constrained by human capacity, leading to uncertainties in diagnoses. Machine learning has the potential to evaluate a larger amount of data and identify patterns and relationships that may otherwise go unnoticed. However, popular machine learning algorithms typically require abundant and labeled data, which is not always available. To address this challenge, the adoption of active learning allows for the selection of the most relevant instances for training, reducing the need for extensive labeling. Additionally, fuzzy logic offers the ability to handle uncertainties. This paper proposes a novel approach that utilizes fuzzy membership functions to transform data as a pre-processing step for active learning. The objective is to approximate similar instances, specifically for the purpose of prediction, thereby minimizing the workload of human experts in labeling data for model training. The results of this study demonstrate the effectiveness of this approach in predicting heart disease and highlight the potential of using membership functions to enhance machine learning models in the analysis of medical information. By incorporating fuzzy logic and active learning, healthcare professionals can benefit from improved accuracy and efficiency in diagnosing and predicting pacients’ health conditions.