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1. A hybrid feature selection algorithm combining information gain and grouping particle swarm optimization for cancer diagnosis.

Author

Yang, Fangyuan, Xu, Zhaozhao, Wang, Hong, Sun, Lisha, Zhai, Mengjiao, and Zhang, Juan

Subjects
FEATURE selection, PARTICLE swarm optimization, MACHINE learning, CANCER diagnosis, ALGORITHMS, SUPPORT vector machines
Abstract

Background: Cancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data. Methods: Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group. Results: Experimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms. Conclusion: The feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Reports from Shiraz University Add New Study Findings to Research in Breast Cancer (Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection).

Subjects
PARTICLE swarm optimization, FEATURE selection, BREAST cancer research, CANCER diagnosis, TECHNOLOGICAL innovations
Abstract

A recent study conducted at Shiraz University aimed to improve the accuracy of breast cancer diagnosis by using a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods. The researchers tested their algorithm on three common breast cancer datasets and found that it significantly improved accuracy while reducing the number of features needed for diagnosis. The study suggests that the proposed algorithm has the potential to enhance breast cancer diagnosis and reduce cost and time. Further information on the study can be found in the International Journal of Computational Intelligence Systems. [Extracted from the article]

Published
2024

3. University of Health Sciences Researcher Publishes Findings in Thyroid Cancer (A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data).

Subjects
MACHINE learning, DIMENSIONAL reduction algorithms, THYROID cancer, CANCER diagnosis, DEEP learning, MISSING data (Statistics)
Abstract

A study conducted by researchers at the University of Health Sciences in Adana, Turkey, has developed a deep machine learning algorithm to diagnose thyroid cancer. The algorithm addresses the challenges of multi-dimensional big data and randomly distributed missing data, which are common in thyroid cancer diagnoses. The algorithm achieved 100% training and 83% testing preciseness for unseen data, and its computational time efficiency was also examined. This research provides valuable insights into the use of machine learning in diagnosing thyroid cancer. [Extracted from the article]

Published
2024

4. Researcher at Kanazawa University Describes Research in Cervical Cancer (A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer).

Subjects
OPTIMIZATION algorithms, FEATURE selection, CERVICAL cancer, RESEARCH personnel, CANCER diagnosis
Abstract

A recent study conducted at Kanazawa University in Japan explores the use of machine learning algorithms for the diagnosis of cervical cancer. The researchers propose a novel feature selection strategy called the Binary Harris Hawks Optimization (BHHO) algorithm, which aims to reduce the number of features used in diagnosis while improving accuracy. The BHHO algorithm demonstrates better stability and accuracy compared to other common wrapper-based feature selection methods, not only on cervical cancer datasets but also on other disease datasets. This research contributes to the ongoing efforts to improve early diagnosis and treatment of cervical cancer, which is one of the leading causes of cancer-related deaths among females worldwide. [Extracted from the article]

Published
2024

5. New Breast Cancer Data Have Been Reported by Investigators at School of Computing (Swarm Intelligent Metaheuristic Optimization Algorithms-based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and...).

Subjects
ARTIFICIAL neural networks, METAHEURISTIC algorithms, CANCER diagnosis, BREAST cancer, OPTIMIZATION algorithms
Abstract

A recent report from investigators at the School of Computing in Tamil Nadu, India, discusses the use of swarm intelligent metaheuristic optimization algorithms-based artificial neural network models for breast cancer diagnosis. The report emphasizes the importance of early detection and the use of optimization algorithms to improve accuracy and efficiency in predicting breast cancer. The researchers compare different approaches and highlight the significance of swarm intelligent algorithms-based optimized artificial neural network models. The report also discusses evaluation metrics and future research directions in this field. This research has been peer-reviewed and provides valuable insights into breast cancer diagnosis. [Extracted from the article]

Published
2024

6. Studies in the Area of Breast Cancer Reported from University of Sydney (Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data...).

Subjects
CANCER diagnosis, IMAGE intensifiers, BREAST cancer, ARTIFICIAL intelligence, MACHINE learning
Abstract

A recent study conducted by researchers at the University of Sydney explored the adaptability of artificial intelligence (AI) models to the Australian context of mammography for breast cancer diagnosis. The study examined the impact of image enhancement on model performance and analyzed the relationship between AI outputs and histopathological features. The results showed that AI models performed better when trained on a subset of mammograms with high concordance between radiologists' annotations and when image enhancement techniques were applied. The study concluded that AI models can enhance the accuracy of breast cancer diagnosis when certain factors, such as concordance classification, image enhancement, and transfer learning, are taken into account. [Extracted from the article]

Published
2024

7. Studies from Chengdu University of Traditional Chinese Medicine Provide New Data on Breast Cancer (Breast Cancer Diagnosis Using Evolving Deep Convolutional Neural Network Based On Hybrid Extreme Learning Machine Technique and Improved Chimp...).

Subjects
CONVOLUTIONAL neural networks, MACHINE learning, CHINESE medicine, CANCER diagnosis, BREAST cancer, SIGNAL convolution
Abstract

A study conducted by researchers at Chengdu University of Traditional Chinese Medicine in Sichuan, China, explores the use of artificial intelligence in diagnosing breast cancer. The researchers propose a new method that utilizes a modified ZFNet network and an extreme learning machine (ELM) to enhance classification performance. The study compares the performance of their method to other well-known methods using various metrics and concludes that their approach, known as ZFNet-SWChOA-ELM, demonstrates the highest level of performance. This research has been peer-reviewed and provides valuable insights into the use of evolving deep convolutional neural networks for breast cancer diagnosis. [Extracted from the article]

Published
2024