scholarly journals Prediction of unsuccessful endometrial ablation: random forest vs logistic regression

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Kelly Yvonne Roger Stevens ◽  
Liesbet Lagaert ◽  
Tom Bakkes ◽  
Malou Evi Gelderblom ◽  
Saskia Houterman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Endometrial Ablation ◽  
Machine Learning Techniques ◽  
Multivariate Logistic Regression Model ◽  
Clinical Prediction ◽  
Clinical Predictors ◽  
Study Objective ◽  
Menopausal Women

Abstract Background Five percent of pre-menopausal women experience abnormal uterine bleeding. Endometrial ablation (EA) is one of the treatment options for this common problem. However, this technique shows a decrease in patient satisfaction and treatment efficacy on the long term. Study objective To develop a prediction model to predict surgical re-intervention (for example re-ablation or hysterectomy) within 2 years after endometrial ablation (EA) by using machine learning (ML). The performance of the developed prediction model was compared with a previously published multivariate logistic regression model (LR). Design This retrospective cohort study, with a minimal follow-up time of 2 years, included 446 pre-menopausal women (18+) that underwent an EA for complaints of heavy menstrual bleeding. The performance of the ML and the LR model was compared using the area under the receiving operating characteristic (ROC) curve. Results We found out that the ML model (AUC of 0.65 (95% CI 0.56–0.74)) is not superior compared to the LR model (AUC of 0.71 (95% CI 0.64–0.78)) in predicting the outcome of surgical re-intervention within 2 years after EA. Based on the ML model, dysmenorrhea and duration of menstruation have the highest impact on the surgical re-intervention rate. Conclusion Although machine learning techniques are gaining popularity in development of clinical prediction tools, this study shows that ML is not necessarily superior to the traditional statistical LR techniques. Both techniques should be considered when developing a clinical prediction model. Both models can identify the clinical predictors to surgical re-intervention and contribute to the shared decision-making process in the clinical practice.

scholarly journals Prediction of Unsuccessful Endometrial Ablation: Random Forest vs Logistic Regression

2020 ◽  
Author(s):  
Kelly Yvonne Roger Stevens ◽  
Liesbet Lagaert ◽  
Tom Bakkes ◽  
Malou Evi Gelderblom ◽  
Saskia Houterman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Treatment Options ◽  
Premenopausal Women ◽  
Endometrial Ablation ◽  
Machine Learning Techniques ◽  
Multivariate Logistic Regression Model ◽  
Clinical Prediction ◽  
Study Objective

Abstract Background: Five percent of premenopausal women experience abnormal uterine bleeding. Endometrial ablation (EA) is one of the treatment options for this common problem. However, this technique shows a decrease in patient satisfaction and treatment efficacy on the long term Study objective: To develop a prediction model to predict surgical re-intervention (for example re-ablation or hysterectomy) within two years after EA by using Machine Learning (ML). The performance of the developed prediction model was compared with a previously published multivariate logistic regression model (LR). Design: This retrospective cohort study, with a minimal follow up time of two years, included 446 pre-menopausal women (18+) that underwent an EA for complaints of heavy menstrual bleeding. The performance of the ML- and the LR model was compared using the area under the Receiving Operating Characteristic (ROC) curve. Results: We found out that the ML model (AUC of 0.65 (95% CI 0.56-0.74)) is not superior compared to the LR model (AUC of 0.71 (95% CI 0.64-0.78)) in predicting the outcome of surgical re-intervention within two years after EA. Conclusion: Although Machine Learning techniques are gaining popularity in development of clinical prediction tools, this study shows that ML is not necessarily superior to the traditional statistical LR techniques. The performance of a prediction model is influenced by the sample size, the number of features of a dataset, hyperparameter tuning and the linearity of associations. Both techniques should be considered when developing a clinical prediction model.

scholarly journals Diving Deeper into Models

2018 ◽  
pp. 121-133
Author(s):  
Alberto Traverso ◽  
Frank J. W. M. Dankers ◽  
Biche Osong ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Unsupervised Learning ◽  
Learning Objectives ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Supervised And Unsupervised Learning

AbstractPre-requisites to better understand the chapter: knowledge of the major steps and procedures of developing a clinical prediction model.Logical position of the chapter with respect to the previous chapter: in the last chapters, you have learned how to develop and validate a clinical prediction model. You have been learning logistic regression as main algorithm to build the model. However, several different more complex algorithms can be used to build a clinical prediction model. In this chapter, the main machine learning based algorithms will be presented to you.Learning objectives: you will be presented with the definitions of: machine learning, supervised and unsupervised learning. The major algorithms for the last two categories will be introduced.

scholarly journals Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260517
Author(s):  
Jee Soo Park ◽  
Dong Wook Kim ◽  
Dongu Lee ◽  
Taeju Lee ◽  
Kyo Chul Koo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Treatment Guidelines ◽  
Ureteral Stone ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Ureteral Stones ◽  
Stone Passage

Objectives To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones. Methods Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework. Results Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5–10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves for MLP, and logistic regression were 0.859 and 0.847, respectively, for stones <5 mm, and 0.881 and 0.817, respectively, for 5–10 mm stones. Conclusion SSP prediction models were developed in patients with well-controlled unilateral ureteral stones; the performance of the models was good, especially in identifying SSP for 5–10-mm ureteral stones without definite treatment guidelines. To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.

Mandibular shape prediction model using machine learning techniques

2022 ◽  
Author(s):  
Tania Camila Niño-Sandoval ◽  
Robinson Andrés Jaque ◽  
Fabio A. González ◽  
Belmiro C. E. Vasconcelos
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Machine Learning Techniques ◽  
Shape Prediction ◽  
Learning Techniques

Abstract MP003: Development and Validation of a Prediction Model of Weight Loss Maintenance or Regain at 4 Years in the Look AHEAD Trial

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Samantha E Berger ◽  
Gordon S Huggins ◽  
Jeanne M McCaffery ◽  
Alice H Lichtenstein
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Weight Loss ◽  
Logistic Regression ◽  
Prediction Model ◽  
Weight Regain ◽  
Initial Weight ◽  
Initial Weight Loss ◽  
Look Ahead

Introduction: The development of type 2 diabetes is strongly associated with excess weight gain and can often be partially ameliorated or reversed by weight loss. While many lifestyle interventions have resulted in successful weight loss, strategies to maintain the weight loss have been considerably less successful. Prior studies have identified multiple predictors of weight regain, but none have synthesized them into one analytic stream. Methods: We developed a prediction model of 4-year weight regain after a one-year lifestyle-induced weight loss intervention followed by a 3 year maintenance intervention in 1791 overweight or obese adults with type 2 diabetes from the Action for Health in Diabetes (Look AHEAD) trial who lost ≥3% of initial weight by the end of year 1. Weight regain was defined as regaining <50% of the weight lost during the intervention by year 4. Using machine learning we integrated factors from several domains, including demographics, psychosocial metrics, health status and behaviors (e.g. physical activity, self-monitoring, medication use and intervention adherence). We used classification trees and stochastic gradient boosting with 10-fold cross validation to develop and internally validate the prediction model. Results: At the end of four years, 928 individuals maintained ≥50% of their initial weight lost (maintainers), whereas 863 did not met that criterion (regainers). We identified an interaction between age and several variables in the model, as well as percent initial weight loss. Several factors were significant predictors of weight regain based on variable importance plots, regardless of age or initial weight loss, such as insurance status, physical function score, baseline BMI, meal replacement use and minutes of exercise recorded during year 1. We also identified several factors that were significant predictors depending on age group (45-55y/ 56-65y/66-76y) and initial weight loss (lost 3-9% vs. ≥10% of initial weight). When the variables identified from machine learning were added to a logistic regression model stratified by age and initial weight loss groups, the models showed good prediction (3-9% initial weight loss, ages 45-55y (n=293): ROC AUC=0.78; ≥10% initial weight loss, ages 45-55y (n=242): ROC AUC=0.78; (3-9% initial weight loss, ages 56-65y (n=484): ROC AUC=0.70; ≥10% initial weight loss, ages 56-65y (n=455): ROC AUC = 0.74; 3-9% initial weight loss, ages 66-76y (n=150): ROC AUC=0.84; ≥10% initial weight loss, ages 66-76y (n=167): ROC AUC=0.86). Conclusion: The combination of machine learning methodology and logistic regression generates a prediction model that can consider numerous factors simultaneously, can be used to predict weight regain in other populations and can assist in the development of better strategies to prevent post-loss regain.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

scholarly journals Machine Learning-Based Prediction Model for Papillary Thyroid Carcinoma Recurrence

2020 ◽  
Author(s):  
Young Min Park ◽  
Byung-Joo Lee
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Tumor Size ◽  
Large Scale ◽  
Prediction Models ◽  
Prognostic Significance ◽  
Disease Recurrence ◽  
Machine Learning Techniques ◽  
Papillary Thyroid ◽  
Recurrence Prediction

Abstract Background: This study analyzed the prognostic significance of nodal factors, including the number of metastatic LNs and LNR, in patients with PTC, and attempted to construct a disease recurrence prediction model using machine learning techniques.Methods: We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with papillary thyroid cancer between 2003 and 2009. Results: We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Conclusions: We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. Large-scale multicenter clinical studies should be performed to improve the performance of our prediction models and verify their clinical effectiveness.

scholarly journals Malicious URL Detection using Logistic Regression

2021 ◽  
Author(s):  
Rohit Rayala ◽  
Sashank Pasumarthi ◽  
Rohith Kuppa ◽  
S R KARTHIK
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
Learning Techniques

Paper is based on a model that is built to detect malicious URLs using machine learning techniques.

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