Journal ArticleDOI
An Efficient, Ensemble-Based Classification Framework for Big Medical Data.
Firoz Khan,Balusupati Veera Venkata Siva Prasad,Salman Ali Syed,Imran Ashraf,Lakshmana Kumar Ramasamy +4 more
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TLDR
In this article, the authors proposed an efficient, ensemble-based classification framework for big medical data to deal with the problem of insufficient classification algorithms for handling big medical datasets, which is a complicated task in the big data age.Abstract:
Fetching useful information from big medical datasets is a complicated task in the big data age. Various classification algorithms are used in the data mining process to analyze information from the big medical dataset. Nevertheless, these classification algorithms are insufficient to handle big medical data. This work proposes an efficient, ensemble-based classification framework for big medical data to deal with this problem. The proposed work involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical data. The process selects a subset of classifiers from a pool of classifiers. The selected classifiers are combined to form a hybrid system for efficient classification. The methodology further involves incremental learning from data samples, explaining the predicted outputs, and achieving high classification performance. Java is used for simulation, and the Cleveland Heart Disease big dataset and Diabetes big dataset are used for classification. The experimental result shows that the proposed ensemble algorithm provides an efficient classification compared with existing algorithms based on accuracy, precision, F-measure, recall, and execution time.read more
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Magnetic Force Classifier: A Novel Method for Big Data Classification
TL;DR: Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space as discussed by the authors .
Journal ArticleDOI
Security and privacy issues in federated healthcare – An overview
TL;DR: The importance of federated learning in healthcare is highlighted and the privacy and security issues in communicating the e-health data are discussed.
Journal ArticleDOI
Clinical Uncertainty Influences Antibiotic Prescribing for Upper Respiratory Tract Infections: A Qualitative Study of Township Hospital Physicians and Village Doctors in Rural Shandong Province, China
TL;DR: Wang et al. as mentioned in this paper explored how clinical uncertainty influences antibiotic prescribing practices among township hospital physicians and village doctors in rural Shandong Province, China, and suggested that interventions to reduce clinical uncertainty may help minimize the unnecessary use of antibiotics in these settings.
Journal ArticleDOI
A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification
Phuoc-Hai Huynh,Van Hoa Nguyen +1 more
TL;DR: In this article , the ensemble approaches based on support vector machines are proposed for classifying medical data, which can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.
References
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Journal ArticleDOI
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
TL;DR: This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners.
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