Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

Panesar, Sandip S.; D'Souza, Rhett N.; Yeh, Fang‐Cheng; Fernández-Miranda, Juan C.

doi:10.1101/472555

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Many concerns need to be answered by ML due to the catastrophic effects of tSCI and the numerous unresolved problems surrounding its management and prognosis [17]. Due to ML's relative novelty and the propensity to focus on a more well-known regression modality instead, there is currently a relatively little body of work on its use to predict outcomes in tSCI [18].…”

Section: Related Workmentioning

confidence: 99%

Design of a novel deep network model for spinal cord injury prediction

Venkatapathi Raju,

Asanambigai,

Babu Mudunuri

2024

IJ-AI

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Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.

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Section: Related Workmentioning

confidence: 99%

Design of a novel deep network model for spinal cord injury prediction

Venkatapathi Raju,

Asanambigai,

Babu Mudunuri

2024

IJ-AI

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“…This gives the opportunity to define a model with predictors which can be used for new and similar data. Compared to statistical logistic regression models, this can be done without a priori assumption of relevant variables (25).…”

Section: Introductionmentioning

confidence: 99%

Prediction of unsuccessful endometrial ablation: Random Forest vs Logistic Regression

et al.

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Objective: To develop a prediction model to predict surgical re-intervention within two years after endometrial ablation (EA) by using a random forest technique (RF). The performance of the developed prediction model was then compared with a previously published multivariate logistic regression model (LR) (1). Design: Retrospective cohort study. Setting: Data from two non-university teaching hospitals in the Netherlands were used. Population: 446 pre-menopausal women who have had an EA for heavy menstrual bleeding between January 2004 and April 2013. Methods: The RF model was trained in MATLAB (2018b) using the TreeBagger function in the Statistics and Machine Learning Toolbox. Main outcome measures: The performance of the two models was compared using the area under the Receiving Operating Characteristic (ROC) curve (AUROC). Measurements and Main Results: The LR model had an AUC of 0.71 (95% CI 0.64-0.78). The RF model had an AUC of 0.63 (95% CI 0.54-0.71). and an AUC of 0.65 (95% CI 0.56-0.74) after hyperparameter optimization. Conclusion: The RF model is not superior compared to the LR model in predicting the outcome of surgical re-intervention within two years after EA. Machine learning techniques are gaining popularity in development of clinical prediction tools, but they are not necessarily superior to traditional statistical logistic regression techniques. The performance of a model is influenced by the sample size and the number of features, hyperparameter tuning and the linearity of associations. Both techniques should be considered when developing a prediction model.

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Enhanced machine learning models for predicting breast cancer: Healthcare system

Mawlood,

Franci,

Hamad

et al. 2024

ITM Web Conf.

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Currently, breast cancer is a popular illness that can lead to many consequences, with the most severe outcome being death rates. Therefore, there is a pressing requirement for a diagnostic tool that can aid healthcare professionals in early detection of the illness and provide required lifestyle modifications to prevent its development the possibility of developing cancer at a young age has also been significantly enhanced by environmental alterations in our daily existence. This analysis aimed to accurately classify features into either malignant or benign classes. The suggested methodologies and classifying systems were applied to the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Conventional performance measures, such as (KNN, SVM, ensemble classifier (EC), and logistic regression (LR)) methods, were utilized to evaluate the efficacy and time of training for each classifier. The diagnostic power of the models was enhanced by our DET (Diagnostic Enhancement Technique). Specifically, the polynomial SVM achieved an accuracy of 98.3%, LR (Logistic Regression) reached 97.04%, KNN (KNearest Neighbors) achieved 96.3%, and EC (Ensemble Classifier) achieved 96.6% accuracy with the dataset is called WDBC. In addition, in this study, there’s just make a comparative analysis of the findings in relation to the accuracy of the outcomes of prior research. The implementation process and results can assist clinicians in adopting an efficient prototype for functional comprehension and forecast of breast cancer (BC) tumours.

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Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

Cited by 5 publications

References 42 publications

Design of a novel deep network model for spinal cord injury prediction

Design of a novel deep network model for spinal cord injury prediction

Prediction of unsuccessful endometrial ablation: Random Forest vs Logistic Regression

Enhanced machine learning models for predicting breast cancer: Healthcare system

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