Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images

• Published in: Medical engineering & physics Vol. 123; p. 104077
• Main Authors: Singh, Law Kumar, Khanna, Munish, Garg, Hitendra, Singh, Rekha
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2024
• Subjects: Algorithms; Computer-assisted diagnosis; Decision Support Systems, Clinical; Feature selection; Fundus Oculi; Glaucoma - diagnostic imaging; Glaucoma screening; Gravitational search algorithm; Humans; Machine Learning; Optimization; Glaucoma screening; Gravitational search algorithm; Feature selection; Computer-assisted diagnosis; Optimization
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2023.104077

•A feature selection strategy which selects the optimal subset of features, by employing GSOA, from the original set is developed, in which the elimination of extraneous features takes place which enhances the classification performance and reduces the computational cost. This improves glaucoma detection while lowering model training computational costs and execution time. The performance is evaluated on the customized dataset of public and private images.•Further in this research, we discuss the application of machine learning in anticipating diseases,thus expanding the field of medical services and bringing in the most recent technology. It is demonstrated how to use the suggested technique, and a comprehensive analysis of different parameters is given. To determine the most effective presentation, various efficiency assessment criteria for many machine learning classifiers were implemented. The parameters included are accuracy, sensitivity,specificity, ROC curves and many more .•Through this study, we offer the best features(selected through soft-computing approach) to researchers, a reliable and effective system of support for medical professionals, and a software-based tool for the human race to slow down this infection spreading through early, quick, and accurate identification of this infection. It might be used in locations with a shortage of qualified medical workers like doctors and nurses. It can also be altered to work with wearable and portable medical devices.•For the detection of this infection from retinal fundus images, our proposed method outperforms existing techniques. This testing procedure will be advantageous for doctors and the state because it is significantly less expensive than other diagnostic instruments used by medical professionals to detect the disease. In addition, owing to its high performance, the proposed medical prediction system can be used to develop mobile applications that aid physicians in the early diagnosis of glaucoma. The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5-fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss.