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Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

• A soft-computing and machine learning based medical decision support system is proposed for breast cancer disease classification. • Two soft-computing algorithms eagle strategy optimization (ESO) algorithm and the gravitational search optimization (GSO) algorithm are employed for the first time for this problem. A novel intelligent classification approach based on these two algorithms for breast cancer infection is also proposed. • 12 experiments have been performed using three soft-computing algorithms(for feature selection) and 6 state-of-the-art machine learning classifier(for classification).WDBC dataset is used for the method evaluation. • We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9696, specificity up to 1.000, precision 1.000, F1- score up to 0.9696, and an AUC 0.9980(close to maximum i.e., 1.0000). Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2n alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases. [ABSTRACT FROM AUTHOR]

Background: The burden of noncommunicable diseases and their risk factors has rapidly increased worldwide, including in India. Innovative management strategies with electronic decision support and task sharing have been assessed for hypertension, diabetes mellitus, and depression individually, but an integrated package for multiple chronic condition management in primary care has not been evaluated.Methods: In a prospective, multicenter, open-label, cluster-randomized controlled trial involving 40 community health centers, using hypertension and diabetes mellitus as entry points, we evaluated the effectiveness of mWellcare, an mHealth system consisting of electronic health record storage and an electronic decision support for the integrated management of 5 chronic conditions (hypertension, diabetes mellitus, current tobacco and alcohol use, and depression) versus enhanced usual care among patients with hypertension and diabetes mellitus in India. At trial end (12-month follow-up), using intention-to-treat analysis, we examined the mean difference between arms in change in systolic blood pressure and glycated hemoglobin as primary outcomes and fasting blood glucose, total cholesterol, predicted 10-year risk of cardiovascular disease, depression score, and proportions reporting tobacco and alcohol use as secondary outcomes. Mixed-effects regression models were used to account for clustering and other confounding variables.Results: Among 3698 enrolled participants across 40 clusters (mean age, 55.1 years; SD, 11 years; 55.2% men), 3324 completed the trial. There was no evidence of difference between the 2 arms for systolic blood pressure (Δ=-0.98; 95% CI, -4.64 to 2.67) and glycated hemoglobin (Δ=0.11; 95% CI, -0.24 to 0.45) even after adjustment of several key variables (adjusted differences for systolic blood pressure: - 0.31 [95% CI, -3.91 to 3.29]; for glycated hemoglobin: 0.08 [95% CI, -0.27 to 0.44]). The mean within-group changes in systolic blood pressure in mWellcare and enhanced usual care were -13.65 mm Hg versus -12.66 mm Hg, respectively, and for glycated hemoglobin were -0.48% and -0.58%, respectively. Similarly, there were no differences in the changes between the 2 groups for tobacco and alcohol use or other secondary outcomes.Conclusions: We did not find an incremental benefit of mWellcare over enhanced usual care in the management of the chronic conditions studied.Clinical Trial Registration: URL: https://www.Clinicaltrials: gov. Unique identifier: NCT02480062. [ABSTRACT FROM AUTHOR]