Differences in the imaging subgroups of cerebral small vessel disease (CSVD) need to be further explored. First, we use propensity score matching to obtain balanced datasets. Then random forest (RF) is adopted to classify the subgroups compared with support vector machine (SVM) and extreme gradient boosting (XGBoost), and to select the features. The top 10 important features are included in the stepwise logistic regression, and the odds ratio (OR) and 95% confidence interval (CI) are obtained. There are 41 290 adult inpatient records diagnosed with CSVD. Accuracy and area under curve (AUC) of RF are close to 0.7, which performs best in classification compared to SVM and XGBoost. OR and 95% CI of hematocrit for white matter lesions (WMLs), lacunes, microbleeds, atrophy, and enlarged perivascular space (EPVS) are 0.9875 (0.9857−0.9893), 0.9728 (0.9705−0.9752), 0.9782 (0.9740−0.9824), 1.0093 (1.0081−1.0106), and 0.9716 (0.9597−0.9832). OR and 95% CI of red cell distribution width for WMLs, lacunes, atrophy, and EPVS are 0.9600 (0.9538−0.9662), 0.9630 (0.9559−0.9702), 1.0751 (1.0686−1.0817), and 0.9304 (0.8864−0.9755). OR and 95% CI of platelet distribution width for WMLs, lacunes, and microbleeds are 1.1796 (1.1636−1.1958), 1.1663 (1.1476−1.1853), and 1.0416 (1.0152−1.0687). This study proposes a new analytical framework to select important clinical markers for CSVD with machine learning based on a common data model, which has low cost, fast speed, large sample size, and continuous data sources.

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.