F-test feature selection in Stacking ensemble model for breast cancer prediction

Dhanya, R.; Paul, Irene Rose; Akula, Sai Sindhu; Sivakumar, M.; Nair, Jyothisha J.

doi:10.1016/j.procs.2020.04.167

Cited by 45 publications

(21 citation statements)

References 10 publications

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“…Taking the LINCS-L1000-CTRP-24h dataset as an example, we compared the WRFEN with the existing traditional methods FTest [ 25 ], MI [ 26 ], RFFS [ 27 ] and LRFS [ 28 ], and tested it on multiple predictors at the same time (Additional file 1 : Fig. S2).…”

Section: Resultsmentioning

confidence: 99%

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Chen

Qin

2021

BMC Bioinformatics

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Background Predicting the drug response of the cancer diseases through the cellular perturbation signatures under the action of specific compounds is very important in personalized medicine. In the process of testing drug responses to the cancer, traditional experimental methods have been greatly hampered by the cost and sample size. At present, the public availability of large amounts of gene expression data makes it a challenging task to use machine learning methods to predict the drug sensitivity. Results In this study, we introduced the WRFEN-XGBoost cell viability prediction algorithm based on LINCS-L1000 cell signatures. We integrated the LINCS-L1000, CTRP and Achilles datasets and adopted a weighted fusion algorithm based on random forest and elastic net for key gene selection. Then the FEBPSO algorithm was introduced into XGBoost learning algorithm to predict the cell viability induced by the drugs. The proposed method was compared with some new methods, and it was found that our model achieved good results with 0.83 Pearson correlation. At the same time, we completed the drug sensitivity validation on the NCI60 and CCLE datasets, which further demonstrated the effectiveness of our method. Conclusions The results showed that our method was conducive to the elucidation of disease mechanisms and the exploration of new therapies, which greatly promoted the progress of clinical medicine.

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Section: Resultsmentioning

confidence: 99%

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Chen

Qin

2021

BMC Bioinformatics

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“…ANOVA stands for “analysis of variance,” It is a parametric statistical hypothesis test that determines if the means from two or more samples of data (usually three or more) originate from the same distribution. An F-statistic, also known as an F-test, is a class of statistical tests that use a statistical test like ANOVA to calculate the ratio between variance values, such as the variance from two separate samples or the explained and unexplained variance [ 38 ]. An ANOVA F-test is a sort of F-statistic that uses the ANOVA approach, and it can be used to identify the top k most relevant features in a feature selection strategy.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches in Covid-19 severity risk prediction in Morocco

Laatifi

Douzi

Bouklouz³

et al. 2022

J Big Data

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The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

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“…The former consider individual features singularly while the latter also account for feature interaction. Among the most common linear approaches, we can list the following: ANOVA F-Test: the Analysis of Variance [7,16,41] method attempts to minimize false negative errors. Chi2 Test: features are ranked according to their Chi-square statistic value.…”

Section: Related Workmentioning

confidence: 99%

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

Dacrema

Moroni

Nembrini

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification.The experimental analysis includes 15 state-of-the-art datasets. The effectiveness obtained with quantum computing hardware is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems. In terms of scalability, current generation quantum computers are able to provide a limited speedup over certain classical algorithms and hybrid quantum-classical strategies show lower computational cost for problems of more than a thousand features. CCS CONCEPTS• Information systems → Content analysis and feature selection; • Computer systems organization → Quantum computing.

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F-test feature selection in Stacking ensemble model for breast cancer prediction

Cited by 45 publications

References 10 publications

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Machine learning approaches in Covid-19 severity risk prediction in Morocco

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

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