MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting

In patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status has been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion-Weighted-Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.

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Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

BMC Anesthesiology ◽

10.1186/s12871-021-01343-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jong Ho Kim ◽

Haewon Kim ◽

Ji Su Jang ◽

Sung Mi Hwang ◽

So Young Lim ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Confidence Interval ◽

Neck Circumference ◽

Difficult Laryngoscopy ◽

Gradient Boosting ◽

Test Set ◽

Equal Distribution ◽

Light Gradient ◽

Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

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An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

Frontiers in Medicine ◽

10.3389/fmed.2021.775047 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ruixia Cui ◽

Wenbo Hua ◽

Kai Qu ◽

Heran Yang ◽

Yingmu Tong ◽

...

Keyword(s):

Machine Learning ◽

Real World ◽

Time Series Data ◽

Time Window ◽

Medical Center ◽

Characteristic Curve ◽

Series Data ◽

Gradient Boosting ◽

Early Management ◽

Extreme Gradient Boosting

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

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Bacterial Immunogenicity Prediction by Machine Learning Methods

Vaccines ◽

10.3390/vaccines8040709 ◽

2020 ◽

Vol 8 (4) ◽

pp. 709

Author(s):

Ivan Dimitrov ◽

Nevena Zaharieva ◽

Irini Doytchinova

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Predictive Ability ◽

Initial Step ◽

Majority Voting ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Test Set ◽

Extreme Gradient Boosting

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

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Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10100680 ◽

2021 ◽

Vol 10 (10) ◽

pp. 680

Author(s):

Annan Yang ◽

Chunmei Wang ◽

Guowei Pang ◽

Yongqing Long ◽

Lei Wang ◽

...

Keyword(s):

Machine Learning ◽

Complex Terrain ◽

Large Scale ◽

Area Under The Curve ◽

Gully Erosion ◽

Susceptibility Mapping ◽

Weight Of Evidence ◽

Gradient Boosting ◽

Machine Learning Classification ◽

Extreme Gradient Boosting

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

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Radiomics with Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients with Prolactinoma

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgab159 ◽

2021 ◽

Author(s):

Yae Won Park ◽

Jihwan Eom ◽

Sooyon Kim ◽

Hwiyoung Kim ◽

Sung Soo Ahn ◽

...

Keyword(s):

Machine Learning ◽

Area Under The Curve ◽

Ensemble Classifier ◽

Magnetic Resonance Images ◽

Gradient Boosting ◽

Test Set ◽

Discrimination Analysis ◽

Severance Hospital ◽

Light Gradient ◽

Ensemble Machine Learning

Abstract Context Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning. Objective To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients. Design Retrospective study Setting Severance Hospital Patients A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA non-responders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set. Results The ensemble classifier showed an area under the curve (AUC) of 0.81 (95 % confidence interval [CI], 0.74–0.87) in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95 % CI, 0.67–0.96), 77.8 %, 78.6 %, and 77.3 %, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set. Conclusions Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.

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The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01480-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jiaxin Fan ◽

Mengying Chen ◽

Jian Luo ◽

Shusen Yang ◽

Jinming Shi ◽

...

Keyword(s):

Machine Learning ◽

Electronic Health Records ◽

Carotid Atherosclerosis ◽

Characteristic Curve ◽

Gradient Boosting ◽

Learning Models ◽

Health Records ◽

Extreme Gradient Boosting ◽

Electronic Health ◽

Machine Learning Models

Abstract Background Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS. Methods Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). Results Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR. Conclusions Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.

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A Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in Kenya

10.20944/preprints202010.0186.v1 ◽

2020 ◽

Author(s):

Nelson Yego ◽

Juma Kasozi ◽

Joseph Nkrunziza

Keyword(s):

Machine Learning ◽

Random Forest ◽

Characteristic Curve ◽

Confusion Matrix ◽

Gradient Boosting ◽

Support Vector ◽

Sampled Data ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

The role of insurance in financial inclusion as well as in economic growth is immense. However, low uptake seems to impede the growth of the sector hence the need for a model that robustly predicts uptake of insurance among potential clients. In this research, we compared the performances of eight (8) machine learning models in predicting the uptake of insurance. The classifiers considered were Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, K Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting Machines and Extreme Gradient boosting. The data used in the classification was from the 2016 Kenya FinAccess Household Survey. Comparison of performance was done for both upsampled and downsampled data due to data imbalance. For upsampled data, Random Forest classifier showed highest accuracy and precision compared to other classifiers but for down sampled data, gradient boosting was optimal. It is noteworthy that for both upsampled and downsampled data, tree-based classifiers were more robust than others in insurance uptake prediction. However, in spite of hyper-parameter optimization, the area under receiver operating characteristic curve remained highest for Random Forest as compared to other tree-based models. Also, the confusion matrix for Random Forest showed least false positives, and highest true positives hence could be construed as the most robust model for predicting the insurance uptake. Finally, the most important feature in predicting uptake was having a bank product hence bancassurance could be said to be a plausible channel of distribution of insurance products.

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Prediction of the Disappearance of Companies From the Market in Bogotá, Colombia Using Machine Learning

Advances in Logistics, Operations, and Management Science - Handbook of Research on Management Techniques and Sustainability Strategies for Handling Disruptive Situations in Corporate Settings ◽

10.4018/978-1-7998-8185-8.ch011 ◽

2021 ◽

pp. 227-246

Author(s):

William Stive Fajardo-Moreno ◽

Rubén Dario Acosta Velásquez ◽

Ivan Dario Castaño Pérez ◽

Leonardo Espinosa-Leal

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Area Under The Curve ◽

Local Economy ◽

Gradient Boosting ◽

Grid Search ◽

Extreme Gradient Boosting ◽

Learning Machine ◽

Available Information ◽

Fold Cross Validation

In this chapter, the results concerning the modeling of companies' disappearance from Bogota's market using machine learning methods are presented. The authors use the available information from Bogota's Chamber of Commerce, where the companies are registered yearly. The dataset comprises the years 2017 to 2020 with almost 3 million registries. In this work, a deep analysis of the different features of the data is presented and explained. Next, four state-of-the-art machine learning models are trained for comparison: logistic regression (LR), extreme learning machine (ELM), random forest (RF), and extreme gradient boosting (XGBoost), all with five-fold cross-validation and 50 steps in the randomized grid search. All methods showed excellent performance, with an average of 0.895 in the area under the curve (AUC), being the latter algorithm the best overall (0.97). These results are in agreement with the state-of-the-art values in the field and will be of paramount importance to assess companies' stability for Bogota's local economy.

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Machine Learning Models for COVID-19 Detection in Brazil Based on Symptoms (Preprint)

10.2196/preprints.27293 ◽

2021 ◽

Author(s):

Íris Viana dos Santos Santana ◽

Andressa C. M. da Silveira ◽

Álvaro Sobrinho ◽

Lenardo Chaves e Silva ◽

Leandro Dias da Silva ◽

...

Keyword(s):

Machine Learning ◽

Early Stage ◽

Area Under The Curve ◽

Supervised Machine Learning ◽

Gradient Boosting ◽

Support Vector ◽

Accuracy Score ◽

K Nearest Neighbors ◽

Runny Nose ◽

Extreme Gradient Boosting

BACKGROUND controlling the COVID-19 outbreak in Brazil is considered a challenge of continental proportions due to the high population and urban density, weak implementation and maintenance of social distancing strategies, and limited testing capabilities. OBJECTIVE to contribute to addressing such a challenge, we present the implementation and evaluation of supervised Machine Learning (ML) models to assist the COVID-19 detection in Brazil based on early-stage symptoms. METHODS firstly, we conducted data preprocessing and applied the Chi-squared test in a Brazilian dataset, mainly composed of early-stage symptoms, to perform statistical analyses. Afterward, we implemented ML models using the Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. We evaluated the ML models using precision, accuracy score, recall, the area under the curve, and the Friedman and Nemenyi tests. Based on the comparison, we grouped the top five ML models and measured feature importance. RESULTS the MLP model presented the highest mean accuracy score, with more than 97.85%, when compared to GBM (> 97.39%), RF (> 97.36%), DT (> 97.07%), XGBoost (> 97.06%), KNN (> 95.14%), and SVM (> 94.27%). Based on the statistical comparison, we grouped MLP, GBM, DT, RF, and XGBoost, as the top five ML models, because the evaluation results are statistically indistinguishable. The ML models` importance of features used during predictions varies from gender, profession, fever, sore throat, dyspnea, olfactory disorder, cough, runny nose, taste disorder, and headache. CONCLUSIONS supervised ML models effectively assist the decision making in medical diagnosis and public administration (e.g., testing strategies), based on early-stage symptoms that do not require advanced and expensive exams.

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A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

10.1101/2020.08.12.248955 ◽

2020 ◽

Author(s):

Liyang Wang ◽

Dantong Niu ◽

Xiaoya Wang ◽

Qun Shen ◽

Yong Xue

Keyword(s):

Machine Learning ◽

High Throughput ◽

High Efficiency ◽

Characteristic Curve ◽

Bovine Milk ◽

Structural Features ◽

Protein Docking ◽

Gradient Boosting ◽

Extreme Gradient Boosting ◽

Antihypertensive Peptides

AbstractStrategies to screen antihypertensive peptides with high throughput and rapid speed will be doubtlessly contributed to the treatment of hypertension. The food-derived antihypertensive peptides can reduce blood pressure without side effects. In present study, a novel model based on Extreme Gradient Boosting (XGBoost) algorithm was developed using the primary structural features of the food-derived peptides, and its performance in the prediction of antihypertensive peptides was compared with the dominating machine learning models. To further reflect the reliability of the method in real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of k-mer peptides cutting from 6 key proteins in bovine milk and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance with the accuracy of 0.9841 and the area under the receiver operating characteristic curve of 0.9428, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using XGBoost algorithm as a novel auxiliary tool is feasible for screening antihypertensive peptide derived from food with high throughput and high efficiency.

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