title: A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.
abstract: A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.
authors: School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia.
authors: Sindhu
authors: 0000-0002-3527-8825
authors: S
authors: Ravindran
authors: School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia.
authors: Asral Bahari
authors: AB
authors: Jambek
authors: School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia.
authors: Hariharan
authors: 0000-0002-8929-3473
authors: H
authors: Muthusamy
authors: Department of Computing Science and Digital Technologies, University of Northumbria, Newcastle NE, UK.
authors: Siew-Chin
authors: SC
authors: Neoh
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D015148:Cardiotocography / Q000379:methods; D020000:Decision Support Systems, Clinical; D005260:Female; D005323:Fetal Monitoring / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D009154:Mutation; D011247:Pregnancy; D015203:Reproducibility of Results; D012984:Software; D013269:Stochastic Processes; D060388:Support Vector Machine