Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data

Abstract

The clinical decision support system provides an automatic diagnosis of human diseases using machine learning techniques to analyze features of patients and classify patients according to different diseases. An analysis of real-world electronic health record (EHR) data has revealed that a patient could be diagnosed as having more than one disease simultaneously. Therefore, to suggest a list of possible diseases, the task of classifying patients is transferred into a multi-label learning task. For most multi-label learning techniques, the class imbalance that exists in EHR data may bring about performance degradation. Cross-Coupling Aggregation (COCOA) is a typical multi-label learning approach that is aimed at leveraging label correlation and exploring class imbalance. For each label, COCOA aggregates the predictive result of a binary-class imbalance classifier corresponding to this label as well as the predictive results of some multi-class imbalance classifiers corresponding to the pairs of this label and other labels. However, class imbalance may still affect a multi-class imbalance learner when the number of a coupling label is too small. To improve the performance of COCOA, a regularized ensemble approach integrated into a multi-class classification process of COCOA named as COCOA-RE is presented in this paper. To provide disease diagnosis, COCOA-RE learns from the available laboratory test reports and essential information of patients and produces a multi-label predictive model. Experiments were performed to validate the effectiveness of the proposed multi-label learning approach, and the proposed approach was implemented in a developed system prototype.

Multi-Label Learning With Label Specific Features Using Correlation Information

“…denotes cardinality representing the average value of labels belonging to instances. In the experimental datasets, the laboratory test results were collected from Haikou People's Hospital in Hainan province, China [39]. The laboratory test results are from 655 patients, and these patients were diagnosed as a least one of diseases including coronary illness, diabetes mellitus type 2, hyperlipemia, anemia, hyperuricemia, chronic kidney disease and cerebral ischemic stroke.…”

Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment

“…In the second study, the authors performed the toddler's nutritional status identification using the clustering method, which is categorized into 5 clusters: good, moderate, malnutrition, over, and obesity. The other study is the enrichment of ontology in tuberculosis epidemiology domain use the pulmonary TB (Tuberculosis) scientific documents [27].…”

Decision Support System for the Production of Miscanthus and Willow Briquettes

“…and agriculture. DSSs in the supply chain are also used in automotive processes, computers, construction, e-commerce, fisheries, food, forestry, logistics, medicine and petroleum [31][32][33]. DSSs are used to supplement energy-related decision processes [34].…”