Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Alfian, Ganjar; Syafrudin, Muhammad; Fahrurrozi, Imam; Fitriyani, Norma Latif; Atmaji, Fransiskus Tatas Dwi; Widodo, Tri; Bahiyah, Nurul; Beneš, Filip; Rhee, Jongtae

doi:10.3390/computers11090136

Cited by 74 publications

(25 citation statements)

References 33 publications

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“…The extra trees algorithm is an ensemble learning method in which each decision tree is constructed from the raw training dataset. Each tree randomly selects k features, each feature randomly selects a split node, and then a score for each split node is calculated based on some mathematical metrics (e.g., the Gini index); the node with the highest score is selected as the final split node [ 19 ]. This random feature selection makes the randomness of each sub model greater, which suppresses the overfitting of the whole model.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys

et al. 2023

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As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosion weight gain prediction model. Eleven classic machine learning algorithms for regression were compared and a ten-fold cross validation method was used to choose the highest accuracy algorithm, which was the extra trees algorithm. Feature selection methods, including the extra trees and Pearson correlation analysis methods, were used to select the most important four factors in corrosion weight gain. As a result, the prediction accuracy of the corrosion weight gain prediction model was 96.8%, which could determine a good prediction of corrosion for uranium and uranium alloys.

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Section: Methodsmentioning

confidence: 99%

A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys

et al. 2023

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“…It takes input and creates multiple trees out of them to separate and select the best attributes according to the execution of the voting algorithm. This mechanism creates decision trees by utilizing the information provided with the input data set and determining an optimum position to divide multiple nodes from each other [35].…”

Section: Extra Tree Classifier (Etc)mentioning

confidence: 99%

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Shivwanshi,

Nirala

2024

Biomed. Phys. Eng. Express

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The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. When applied to a prominent feature space, traditional techniques may also struggle with combinatorial optimization problems. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quadratic unconstrained binary optimization (FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

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“…Artificial intelligence (AI) and machine learning (ML)-based diagnostics have been widely utilized by researchers and practitioners to detect risks and facilitate decision-making in a range of contexts, from the prediction of chronic diseases like cancer [70,71] to psychological disorders [72]. Therefore, this ML-based deep learning model yield in this experiment can be further implemented in web/mobile-based applications for practical usage and gives maximum benefits of early glaucoma detection to the worldwide community.…”

Section: Practical Applicationmentioning

confidence: 99%

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

et al. 2022

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Recently, the development of a rapid detection approach for glaucoma has been widely proposed to assist medical personnel in detecting glaucoma disease thanks to the outstanding performance of artificial intelligence. In several glaucoma detectors, cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) play roles as the major objects that are used to analyze glaucoma. However, using CDR and DDLS is quite difficult since every person has different characteristics (shape, size, etc.) of the optic disc and optic cup. To overcome this issue, we proposed an alternative way to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL). Our proposed method is divided into two processes: (1) the pre-treatment process and (2) the glaucoma classification process. We started the pre-treatment process by removing unnecessary parts, such as the optic disc and blood vessels. Both parts are considered for removal since they might be obstacles during the analysis process. For the classification stages, we used nine deep-learning architectures. We evaluated our proposed method in the ORIGA dataset and achieved the highest accuracy of 92.88% with an AUC of 89.34%. This result is improved by more than 15% from the previous research work. Finally, it is expected that our model could help improve eye disease diagnosis and assessment.

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Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Cited by 74 publications

References 33 publications

A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys

A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

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