A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data

2021 ◽  
pp. 103763
Author(s):  
Jaime Lynn Speiser
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Longitudinal Data ◽  
Prediction Models ◽  
Random Forest Method ◽  
Selection For

scholarly journals FEATURE SELECTION FOR AUTOMATIC DETECTION OF TEXT GENRE

2021 ◽  
pp. 84-90
Author(s):  
S. Sh. Shanshar ◽  
I. M. Ualiyeva
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Information Society ◽  
Automatic Detection ◽  
Practical Method ◽  
Automatic Recognition ◽  
Logistics Regression ◽  
Text Genre ◽  
Selection For

This article discusses the algorithms that can be used in the study and analysis of symbols to determine the genre of texts. There are differences in defining the genre of texts. Algorithm is also defined by describing the text, removing unnecessary characters, leaving only the text, and comparing it with the database. The article describes a practical method of automatic recognition of the text genre based on all parameters. Comparing the logistics regression, solution tree, random forest, MLPClassifier, AdaBoostClassifier, svm, GaussianNB algorithms, the choice of the most important parameters for the texts was considered. Defining the genre of texts is now relevant in all areas of the information society.

scholarly journals Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

2021 ◽  
Vol 18 (20) ◽  
pp. 10670
Author(s):  
Nahúm Cueto López ◽  
María Teresa García-Ordás ◽  
Facundo Vitelli-Storelli ◽  
Pablo Fernández-Navarro ◽  
Camilo Palazuelos ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Public Health Problem ◽  
Healthy Population ◽  
Major Public Health Problem ◽  
The Stability

This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.

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