Application of Nature Inspired Soft Computing Techniques for Gene Selection: A Novel Frame Work for Classification of Cancer.

Aziz, Rabia Musheer

doi:10.21203/rs.3.rs-1121838/v1

Cited by 8 publications

(4 citation statements)

References 45 publications

(13 reference statements)

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“…The proposed work was benchmarked on four benchmark datasets where it showed significant improvement in fitness value when compared with the swarm intelligence technique and various bi-clustering algorithms [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Metaheuristic Model of Gene Selection for Deep Learning Early Prediction of Cancer Disease Using Gene Expression Data

Joshi

Aziz

2023

Preprint

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Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of prediction algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, proposed a hybrid novel technique CSSMO, based feature selection for cancer prediction. First, combine the use of Spider Monkey Optimization (SMO) along with Cuckoo search (CS) algorithm viz. CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Next, these subsets of genes are classified using Deep Learning (DL) to identify different groups or classes related to a particular cancer disease. Six different datasets have utilized to analyze the performance of the proposed approach in terms of cancer sample classification and prediction with Recall, Precision, F1-Score, and confusion matrix. Proposed gene selection method with DL achieves much better prediction accuracy than other existing Deep Learning (DL) and Machine learning models with a large gene expression datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Metaheuristic Model of Gene Selection for Deep Learning Early Prediction of Cancer Disease Using Gene Expression Data

Joshi

Aziz

2023

Preprint

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“…The study contributes to high accuracy performance compared to the previously published feature selection techniques. The classification of cancer-based on gene expression using a novel framework was proposed [29]. The ABC-based modified metaheuristics optimization technique was applied for the classification task.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

et al. 2022

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Polycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase. The PCOS disorder is commonly caused by excess male hormone and androgen levels. The follicles are the collections of fluid developed by ovaries and may fail to release eggs regularly. The PCOS results in miscarriage, infertility issues, and complications during pregnancy. According to a recent report, PCOS is diagnosed in 31.3% of women from Asia. Studies show that 69% to 70% of women did not avail of a detecting cure for PCOS. A research study is needed to save women from critical complications by identifying PCOS early. The main aim of our research is to predict PCOS using advanced machine learning techniques. The dataset based on clinical and physical parameters of women is utilized for building study models. A novel feature selection approach is proposed based on the optimized chi-squared (CS-PCOS) mechanism. The ten hyper-parametrized machine learning models are applied in comparison. Using the novel CS-PCOS approach, the gaussian naive bayes (GNB) outperformed machine learning models and state-of-the-art studies. The GNB achieved 100% accuracy, precision, recall, and f1-scores with minimal time computations of 0.002 seconds. The k-fold cross-validation of GNB achieved a 100% accuracy score. The proposed GNB model achieved accurate results for critical PCOS prediction. Our study reveals that the dataset features prolactin (PRL), blood pressure systolic, blood pressure diastolic, thyroid stimulating hormone (TSH), relative risk (RR-breaths), and pregnancy are the prominent factors having high involvement in PCOS prediction. Our research study helps the medical community overcome the miscarriage rate and provide a cure to women through the early detection of PCOS.

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“…In addition, the models received 94.10 percent for weighted recalls, accuracy, and F1-scores, and 94.10 percent for weighted recalls, accuracy, and F1scores. Table 7 shows a comparison of the model with and without feature selection [37][38][39][40][41][42][43][44][45][46][47].…”

Section: Description Of K-fold Cross-validationmentioning

confidence: 99%

Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information

Devi

Karthikeyan

Moorthy

et al. 2022

Mathematical Problems in Engineering

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Microarray data are becoming a more essential source of gene expression data for interpretation and analysis. To improve the detection accuracy of tumors, the researchers try to use the lowest feasible collection of the most gene expression studies, and relevant gene expression patterns are found. The purpose of this article is to use a data mining strategy and an optimized feature selection method focused on a limited dense tree forest classifier to evaluate and forecast colon cancer data. More specifically, merging the “gain information” and “Grey wolf optimization” was incorporated as a feature selection approach into the random forest classifier, to improve the prediction model’s accuracy. Our suggested technique can decrease the load of high-dimensional data, and it allows quicker computations. In this research, we provided a comparison of the analysis model with feature selection accuracy over model analysis without feature selection accuracy. The extensive experimental findings have shown that the suggested method with selecting features is beneficial, outperforming the good classification performances.

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Application of Nature Inspired Soft Computing Techniques for Gene Selection: A Novel Frame Work for Classification of Cancer.

Cited by 8 publications

References 45 publications

Metaheuristic Model of Gene Selection for Deep Learning Early Prediction of Cancer Disease Using Gene Expression Data

Metaheuristic Model of Gene Selection for Deep Learning Early Prediction of Cancer Disease Using Gene Expression Data

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information

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