Radiogenomic modeling predicts survival-associated prognostic groups in glioblastoma

Abstract Background Combined whole-exome sequencing (WES) and somatic copy number alteration (SCNA) information can separate isocitrate dehydrogenase (IDH)1/2-wildtype glioblastoma into two prognostic molecular subtypes, which cannot be distinguished by epigenetic or clinical features. The potential for radiographic features to discriminate between these molecular subtypes has yet to be established. Methods Radiologic features (n = 35 340) were extracted from 46 multisequence, pre-operative magnetic resonance imaging (MRI) scans of IDH1/2-wildtype glioblastoma patients from The Cancer Imaging Archive (TCIA), all of whom have corresponding WES/SCNA data. We developed a novel feature selection method that leverages the structure of extracted MRI features to mitigate the dimensionality challenge posed by the disparity between a large number of features and the limited patients in our cohort. Six traditional machine learning classifiers were trained to distinguish molecular subtypes using our feature selection method, which was compared to least absolute shrinkage and selection operator (LASSO) feature selection, recursive feature elimination, and variance thresholding. Results We were able to classify glioblastomas into two prognostic subgroups with a cross-validated area under the curve score of 0.80 (±0.03) using ridge logistic regression on the 15-dimensional principle component analysis (PCA) embedding of the features selected by our novel feature selection method. An interrogation of the selected features suggested that features describing contours in the T2 signal abnormality region on the T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI sequence may best distinguish these two groups from one another. Conclusions We successfully trained a machine learning model that allows for relevant targeted feature extraction from standard MRI to accurately predict molecularly-defined risk-stratifying IDH1/2-wildtype glioblastoma patient groups.

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NIMG-46. RADIOGENOMIC FEATURES PREDICT CLINICALLY RELEVANT GENOME-WIDE ALTERATION SIGNATURES IN GLIOBLASTOMA

Neuro-Oncology ◽

10.1093/neuonc/noaa215.659 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii158-ii158

Author(s):

Nicholas Nuechterlein ◽

Beibin Li ◽

James Fink ◽

David Haynor ◽

Eric Holland ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Molecular Subtypes ◽

Feature Selection Method ◽

Selection Method ◽

Versus Group ◽

Mri Features ◽

Genome Wide ◽

Group 2 ◽

Group 1

Abstract BACKGROUND Previously, we have shown that combined whole-exome sequencing (WES) and genome-wide somatic copy number alteration (SCNA) information can separate IDH1/2-wildtype glioblastoma into two prognostic molecular subtypes (Group 1 and Group 2) and that these subtypes cannot be distinguished by epigenetic or clinical features. However, the potential for radiographic features to discriminate between these molecular subtypes has not been established. METHODS Radiogenomic features (n=35,400) were extracted from 46 multiparametric, pre-operative magnetic resonance imaging (MRI) of IDH1/2-wildtype glioblastoma patients from The Cancer Imaging Archive, all of whom have corresponding WES and SCNA data in The Cancer Genome Atlas. We developed a novel feature selection method that leverages the structure of extracted radiogenomic MRI features to mitigate the dimensionality challenge posed by the disparity between the number of features and patients in our cohort. Seven traditional machine learning classifiers were trained to distinguish Group 1 versus Group 2 using our feature selection method. Our feature selection was compared to lasso feature selection, recursive feature elimination, and variance thresholding. RESULTS We are able to classify Group 1 versus Group 2 glioblastomas with a cross-validated area under the curve (AUC) score of 0.82 using ridge logistic regression and our proposed feature selection method, which reduces the size of our feature set from 35,400 to 288. An interrogation of the selected features suggests that features describing contours in the T2 abnormality region on the FLAIR MRI modality may best distinguish these two groups from one another. CONCLUSIONS We successfully trained a machine learning model that allows for relevant targeted feature extraction from standard MRI to accurately predict molecularly-defined risk-stratifying IDH1/2-wildtype glioblastoma patient groups. This algorithm may be applied to future prospective studies to assess the utility of MRI as a surrogate for costly prognostic genomic studies.

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Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

10.21203/rs.3.rs-742641/v1 ◽

2021 ◽

Author(s):

K venkatachalam ◽

P Prabhu ◽

B saravana Balaji ◽

Mohamed Abouhawwash ◽

R Rajadevi

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Ridge Regression ◽

Feature Selection Method ◽

Selection Method ◽

Recursive Feature Elimination ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Data Set

Abstract In day today life, diabetes illness is increasing in count due to the body not able to metabolize the glucose level. The prediction of the right diabetes patients is an important research area that many researchers are proposing the techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is one of the key concept in preprocessing so that the features that are relevant to the disease will be used for prediction. This will improve the prediction accuracy. Selecting right features among the whole feature set is a complicated process and many researchers are concentrating on it to produce the predictive model with high accuracy. In this proposed work, the wrapper based feature selection method called Recursive Feature Elimination (RFE) is combined with Ridge regression (L2) to form a hybrid L2 regulated feature selection algorithm to overcome the overfilling problem of the data set. Over fitting is the major problem in feature selection which means that the new data are not fit to the model since the training data is small. Ridge regression is mainly used to overcome the overfitting problem. Once the features are selected using the proposed feature selection method, random forest classifier is used to classify the data based on the selected features. The proposed work is experimented in PIDD data set and the evaluated results are compared with the existing algorithms to prove the accuracy effect of the proposed algorithm. From the results obtained by proposed algorithm, the accuracy of predicting the diabetes disease is high compared to other existing algorithms.

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An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection

Mathematics ◽

10.3390/math9111226 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1226

Author(s):

Saeed Najafi-Zangeneh ◽

Naser Shams-Gharneh ◽

Ali Arjomandi-Nezhad ◽

Sarfaraz Hashemkhani Zolfani

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Standard Deviation ◽

Analytical Formula ◽

Feature Selection Method ◽

Selection Method ◽

Performance Measure ◽

Learning Approaches ◽

Training Costs ◽

Professional Employees

Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions. Unlike physical systems, human resource problems cannot be described by a scientific-analytical formula. Therefore, machine learning approaches are the best tools for this aim. This paper presents a three-stage (pre-processing, processing, post-processing) framework for attrition prediction. An IBM HR dataset is chosen as the case study. Since there are several features in the dataset, the “max-out” feature selection method is proposed for dimension reduction in the pre-processing stage. This method is implemented for the IBM HR dataset. The coefficient of each feature in the logistic regression model shows the importance of the feature in attrition prediction. The results show improvement in the F1-score performance measure due to the “max-out” feature selection method. Finally, the validity of parameters is checked by training the model for multiple bootstrap datasets. Then, the average and standard deviation of parameters are analyzed to check the confidence value of the model’s parameters and their stability. The small standard deviation of parameters indicates that the model is stable and is more likely to generalize well.

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