An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine

Kollem, Sreedhar

doi:10.1007/s11042-024-18233-9

Cited by 4 publications

(1 citation statement)

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“…GAs employ a population of solutions from which increasingly superior solutions can be derived via recombination and selection processes [22]. SVMs are learning algorithms that employ a hypothesis space of linear functions in a high-dimensional feature set [23]. It is noted that XGBoost is without feature selection.…”

Section: Resultsmentioning

confidence: 99%

A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm

Lee,

Yang,

Hwang

2024

Diagnostics

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Asthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic losses due to the degradation of patients’ quality of life and the impairment of their physical fitness. Asthma research has evolved in recent years to fully analyze why certain diseases develop based on a variety of data and observations of patients’ performance. The advent of new techniques offers good opportunities and application prospects for the development of asthma diagnosis methods. Over the last few decades, techniques like data mining and machine learning have been utilized to diagnose asthma. Nevertheless, these traditional methods are unable to address all of the difficulties associated with improving a small dataset to increase its quantity, quality, and feature space complexity at the same time. In this study, we propose a sustainable approach to asthma diagnosis using advanced machine learning techniques. To be more specific, we use feature selection to find the most important features, data augmentation to improve the dataset’s resilience, and the extreme gradient boosting algorithm for classification. Data augmentation in the proposed method involves generating synthetic samples to increase the size of the training dataset, which is then utilized to enhance the training data initially. This could lessen the phenomenon of imbalanced data related to asthma. Then, to improve diagnosis accuracy and prioritize significant features, the extreme gradient boosting technique is used. The outcomes indicate that the proposed approach performs better in terms of diagnostic accuracy than current techniques. Furthermore, five essential features are extracted to help physicians diagnose asthma.

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Section: Resultsmentioning

confidence: 99%