Evolutionary feature transformation to improve prognostic prediction of hepatitis

• Published in: Knowledge-based systems Vol. 200; p. 106012
• Main Authors: Parisi, Luca, RaviChandran, Narrendar
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 20.07.2020; Elsevier Science Ltd
• Subjects: Classification; Classifiers; Decision support system; Decision support systems; Evolutionary algorithms; Feature augmentation; Feature selection; Genetic algorithm; Genetic algorithms; Hepatitis; Interpolation; Machine learning; Prognosis; Reduction; Reliability; Hepatitis; Evolutionary algorithms; Decision support system; Feature augmentation; Genetic algorithm; Machine learning
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2020.106012

Despite advances in Machine Learning (ML) algorithms, the clinical viability of ML-based decision support systems (DSS) to predict the prognosis of hepatitis remains limited. However, an appropriate feature selection could improve its reliability. Differently from conventional feature reduction methods, we hypothesised that applying feature reduction first and then augmenting the reduced feature space could improve classification performance further. Thus, a novel two-stage Genetic Algorithm (GA)-based feature transformation method, which involves both feature reduction and augmentation (2-Tra-GA), was developed, tested and validated. This 2-Tra-GA was later coupled to ML-based classifiers for a prognostic prediction. Clinical data with nineteen (N = 19) features on 320 patients with hepatitis obtained from the University of California-Irvine ML repository were utilised. When tested on these data, the GA-based feature reduction resulted in a reduced set with fifteen (N=15) features that led to the highest classification accuracy and reliability. Augmenting the reduced set by adding transformed features via an interpolation method (N=32 features in total, 15 reduced and 17 transformed) further improved the classification performance. Additionally, the performance of this novel hybrid algorithm was evaluated against classifiers alike and published studies. Applying feature reduction, then augmenting only such relevant features improved classification performance and computational efficiency, also over conventional wrapper-based feature selection methods. Thus, a novel hybrid DSS to improve the reliability of the prediction of prognosis for hepatitis is proposed. Findings also support the application of the proposed hybrid method to improve clinical decision making. •Our novel feature selection can improve prognosis of hepatitis.•We used an evolutionary method for feature augmentation.•We assessed its performance with state-of-the-art classifiers.•Our feature selection method improves prediction of survival.