Logistic Regression Decision Tree Research Articles

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurrence prediction. A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single-center retrospective study and were randomly divided into the training dataset (n = 870) and the test dataset (n = 217). Univariate logistic regression analysis was used to identify risk factors that associated with the occurrence of postoperative VTE in the training dataset. Machine learning models (including decision tree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated. The incidence of developing 30-day postoperative VTE was 6.0% (65/1087). Age, previous VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lymphadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G-Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p < 0.05). The AUCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respectively. The ML models, especially the DT model, constructed in our study had excellent prediction value and shed light upon its further application in clinic practice.

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.

Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models. This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts. The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

The value of a dual-energy CT Iodine map radiomics model for the prediction of collagen fiber content in the ccRCC tumor microenvironment

Background and purposeRenal cell carcinoma (RCC) is a heterogeneous group of cancers. The collagen fiber content in the tumor microenvironment of renal cancer has an important role in tumor progression and prognosis. A radiomics model was developed from dual-energy CT iodine maps to assess collagen fiber content in the tumor microenvironment of ccRCC.MethodsA total of 87 patients with ccRCC admitted to our hospital were included in this retrospective study. Among them, 59 cases contained large amounts of collagen fibers and 28 cases contained a small amount of collagen fibers. We established a radiomics model using preoperative dual-energy CT scan Iodine map (IV) imaging to distinguish patients with multiple collagen fibers from those with few collagen fibers in the tumor microenvironment of ccRCC. We extracted features from dual-energy CT Iodine map images to evaluate the effects of six classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT). The effects of the models built based on the dynamic and venous phases are also compared. Model performance was evaluated using quintuple cross-validation and area under the receiver operating characteristic curve (AUC). In addition, a clinical model was developed to assess the clinical factors affecting collagen fiber content.ResultsCompared to KNN, SVM, and LR classifiers, RF, DT, and XGBoost classifiers trained with higher AUC values, with training sets of 0.997, 1.0, and 1.0, respectively. In the validation set, the highest AUC was found in the SVM classifier with a size of 0.722. In the comparative test of the active and intravenous phase models, the SVM classifier had the best effect with its validation set AUC of 0.698 and 0.741. In addition, there was a statistically significant effect of patient age and maximum tumor diameter on the collagen fiber content in the tumor microenvironment of kidney cancer.ConclusionRadionics features based on preoperative dual-energy CT IV can be used to predict the amount of collagen fibers in the tumor microenvironment of renal cancer. This study better informs clinical prognosis and patient management. Iodograms may add additional value to dual-energy CTs.

A novel stacking framework with PSO optimized SVM for effective disease classification

Disease diagnosis is very important in the medical field. It is essential to diagnose chronic diseases such as diabetes, heart disease, cancer, and kidney diseases in the early stage. In recent times, ensembled-based approaches giving effective predictive performance than individual classifiers and gained attention in assisting doctors with early diagnosis. But one of the challenges in these approaches is dealing with class-imbalanced data and improper configuration of ensemble classifiers with optimized parameters. In this paper, a novel 3-level stacking approach with ADASYN oversampling technique with PSO Optimized SVM meta-model (Stacked-ADASYN-PSO) is proposed. Our proposed Stacked-ADASYN-PSO model uses base models such as Logistic regression(LR), K-Nearest neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP) in layer-0. In layer-1 three meta classifiers namely LR, KNN, and Bagging DT are used. In layer-2 PSO optimized SVM used as the final meta-model to combine the previous layer predictions. To evaluate the robustness of the proposed model It is tested on five benchmark disease datasets from the UCI machine learning repository. These results are compared with state-of-the-art ensemble models and non-ensemble models. Results demonstrated that the proposed model performance is superior in terms of AUC, accuracy, specificity, and precision. We have performed statistical analysis using paired T-tests with a 95% confidence level and our proposed stacking model is significantly differs when compared to base classifiers.

Improvement of the Performance of Machine Learning Algorithms in Predicting Breast Cancer

Introduction: Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for tumor classification prediction.Materials &amp; Methods: Several supervised machine learning algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this paper ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.Results: As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.Conclusions: The technique of machine learning can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.

Detection of Hate Speech Text in Afan Oromo Social Media using Machine Learning Approach

Objectives: This study aims to develop a hate speech detection model for Afan Oromo’s texts on social networks like Facebook and Twitter using a machine learning algorithm. Methods: we collected comments and posts from social media like Facebook and Twitter pages of BBC Afan Oromo, OBN Afan Oromo, Fana Afan Oromo Program, Politicians, Activists, Religious Men, and Oromia Communication Bureau using Face pager tool. The collected data was labelled using Afan Oromo hate speech evaluation system we developed. Text preprocessing tasks applied on data to remove special characters, stop-words,HTML Tags, extra whitespaces, numbers, lemmatization. The n-gram and TFIDF was applied for feature extraction task to obtain benchmark Afan Oromo hate speech detection dataset. Researchers split dataset into train and test set. Finally, we applied Support Vector Classifier, Multinomial NB, Linear Support Vector Classifier, Logistic Regression decision tree and Random Forest Classifier on 67% of trained data. The performance of proposed model also evaluated using F-score. We also test the performance of developed model by loading test set into it. Findings: Hate speech on social media violates the welfare of Ethnic groups and citizens for living together. Many researches have been doing for English, Amharic, and other Languages to detect hate content from social media. This study has focused on developing a prototype for Afan Oromo hate speech detection model using machine learning algorithms and evaluate its performance in which we found Linear Support Vector Classifier scored highest f1-score value is 64%. Novelty: Afan Oromo hate speech detection framework proposed and successfully implemented to develop Afan Oromo hate speech detection model. We wrote python script that overcome problems typos in Afan Oromo in addition to designing python scripts that recognized apostrophe “ ’ ” as important letter for Afan Oromo word formation. Yet, no researchers have used combination of n-gram and TF-IDF for feature extraction. In this study, the n-gram and TF-IDF used for feature extraction approach to build model that detect Afan Oromo hate speech on Social media. Keywords: Afan Oromo; Decision tree; Facebook; Hate Speech; Linear Support Vector Classifier; Machine Learning; MultinomialNB; Social Media; Support Vector Classifier; Decision Tree and Random Forest Classifier