scholarly journals Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6013
Author(s):  
Hyun-Soo Park ◽  
Kwang-sig Lee ◽  
Bo-Kyoung Seo ◽  
Eun-Sil Kim ◽  
Kyu-Ran Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Cancer Patients ◽  
Low Dose ◽  
Random Forest Model ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Forest Model ◽  
Machine Learning Models

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

scholarly journals Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Eun Kyung Park ◽  
Kwang-sig Lee ◽  
Bo Kyoung Seo ◽  
Kyu Ran Cho ◽  
Ok Hee Woo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Invasive Breast Cancer ◽  
Low Dose ◽  
Molecular Subtypes ◽  
Random Forest Model ◽  
Prognostic Biomarkers ◽  
Learning Approaches ◽  
Forest Model

AbstractRadiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer.

scholarly journals Detecting Myocardial Infarction by Electrocardiogram Machine Learning Models With Greater Accuracy; A Technical Advance Article

2021 ◽  
Author(s):  
M.D.S. Sudaraka ◽  
I. Abeyagunawardena ◽  
E. S. De Silva ◽  
S Abeyagunawardena
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Random Forest ◽  
Decision Trees ◽  
Sensitivity And Specificity ◽  
Random Forest Model ◽  
Multi Layer Perceptron ◽  
Learning Models ◽  
Forest Model ◽  
Machine Learning Models

Abstract BackgroundElectrocardiogram (ECG) is a key diagnostic test in cardiac investigation. Interpretation of ECG is based on the understanding of normal electrical patterns produced by the heart and alterations of those patterns in specific disease conditions. With machine learning techniques, it is possible to interpret ECGs with increased accuracy. However, there is a lacuna in machine learning models to detect myocardial infarction (MI) coupled with the affected territories of the heart. MethodsThe dataset was obtained from the University of California, Irvine, Machine Learning Repository. It was filtered to obtain observations categorized as Normal, Ischemic changes, Old Anterior MI and Old Inferior MI. The dataset was randomly split into a training set (70%) and a test set (30%). 73 out of the 270 ECG features were selected based on the changes observed following MI, after excluding predictors that had near zero variance across the observations. Three machine learning classification models (Bootstrap Aggregation Decision Trees, Random Forest, Multi-layer Perceptron) were trained using the training dataset, optimizing for the Kappa statistic and the parameter tuning was achieved with repeated 10-fold cross validation. Accuracy and Kappa of the samples were used to evaluate performance between the models. ResultsThe Random Forest model identified old anterior and old inferior MIs with 100% sensitivity and specificity and all 4 categorized observations with an overall accuracy of 0.9167 (95% CI 0.8424 - 0.9633). Both the Bootstrap Aggregation Decision Trees and the Multi-layer Perceptron models identified old anterior MIs with 100% sensitivity and specificity and their overall accuracies for all 4 observations were 0.8958 (95% CI 0.8168 - 0.9489) and 0.8542 (95% CI 0.7674 - 0.9179) respectively.Conclusion With a medically informed feature selection we were able to identify old anterior MI with 100% sensitivity and specificity by all three models in this study, and old inferior MI with 100% sensitivity and specificity by Random Forest Model. If the data set can be improved it is possible to utilize these machine learning models in hospital setting to identify cardiac emergencies by incorporating them into cardiac monitors, until trained personnel become available.

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

Data ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 116
Author(s):  
Nelson Kemboi Yego ◽  
Juma Kasozi ◽  
Joseph Nkurunziza
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Phase I ◽  
Survey Data ◽  
Low Income ◽  
Random Forest Model ◽  
Learning Models ◽  
Forest Model ◽  
Learning Classifiers ◽  
Machine Learning Models

The role of insurance in financial inclusion and economic growth, in general, is immense and is increasingly being recognized. However, low uptake impedes the growth of the sector, hence the need for a model that robustly predicts insurance uptake among potential clients. This study undertook a two phase comparison of machine learning classifiers. Phase I had eight machine learning models compared for their performance in predicting the insurance uptake using 2016 Kenya FinAccessHousehold Survey data. Taking Phase I as a base in Phase II, random forest and XGBoost were compared with four deep learning classifiers using 2019 Kenya FinAccess Household Survey data. The random forest model trained on oversampled data showed the highest F1-score, accuracy, and precision. The area under the receiver operating characteristic curve was furthermore highest for random forest; hence, it could be construed as the most robust model for predicting the insurance uptake. Finally, the most important features in predicting insurance uptake as extracted from the random forest model were income, bank usage, and ability and willingness to support others. Hence, there is a need for a design and distribution of low income based products, and bancassurance could be said to be a plausible channel for the distribution of insurance products.

Machine Learning based Tissue Analysis Reveals Brachyury has a Diagnosis Value in Breast Cancer

2020 ◽  
Author(s):  
Kaichun Li ◽  
Qiaoyun Wang ◽  
Yanyan Lu ◽  
Xiaorong Pan ◽  
Long Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Cancer Patients ◽  
Decision Tree Model ◽  
Superior Performance ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Tree Model ◽  
Machine Learning Models

Abstract Background The aim of this study was to confirm the role of Brachyury in breast cells and to establish and verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected a total of 303 patients for research and implemented four machine learning prediction algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).Conclusions Brachyury is highly expressed in breast cancer and indicates that the patient had a poor chance of survival. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients. This indicates that machine learning can thus be applied in a wide range of clinical studies.

scholarly journals Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer

2021 ◽  
Author(s):  
Kaichun Li ◽  
Qiaoyun Wang ◽  
Yanyan Lu ◽  
Xiaorong Pan ◽  
Long Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Cancer Patients ◽  
Decision Tree Model ◽  
Superior Performance ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Tree Model ◽  
Machine Learning Models

Background The aim of this study was to confirm the role of Brachyury in breast cancer and to verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.</p>  Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected  303 patients for research and implemented four machine learning algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.</p>  Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).</p>  Conclusions Brachyury is highly expressed in breast cancer and indicates that patients had a poor prognosis. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients.

scholarly journals Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yadi Zhu ◽  
Ling Yang ◽  
Hailin Shen
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Preoperative Prediction ◽  
Machine Learning Model ◽  
Validation Set ◽  
Machine Learning Models

PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

scholarly journals Design of Machine Learning Prediction System Based on the Internet of Things Framework for Monitoring Fine PM Concentrations

Environments ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 99
Author(s):  
Shun-Yuan Wang ◽  
Wen-Bin Lin ◽  
Yu-Chieh Shu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Particulate Matter ◽  
Random Forest ◽  
Internet Of Things ◽  
Random Forest Model ◽  
The Internet ◽  
Learning Models ◽  
Forest Model ◽  
The Internet Of Things

In this study, a mobile air pollution sensing unit based on the Internet of Things framework was designed for monitoring the concentration of fine particulate matter in three urban areas. This unit was developed using the NodeMCU-32S microcontroller, PMS5003-G5 (particulate matter sensing module), and Ublox NEO-6M V2 (GPS positioning module). The sensing unit transmits data of the particulate matter concentration and coordinates of a polluted location to the backend server through 3G and 4G telecommunication networks for data collection. This system will complement the government’s PM2.5 data acquisition system. Mobile monitoring stations meet the air pollution monitoring needs of some areas that require special observation. For example, an AIoT development system will be installed. At intersections with intensive traffic, it can be used as a reference for government transportation departments or environmental inspection departments for environmental quality monitoring or evacuation of traffic flow. Furthermore, the particulate matter distributions in three areas, namely Xinzhuang, Sanchong, and Luzhou Districts, which are all in New Taipei City of Taiwan, were estimated using machine learning models, the data of stationary monitoring stations, and the measurements of the mobile sensing system proposed in this study. Four types of learning models were trained, namely the decision tree, random forest, multilayer perceptron, and radial basis function neural network, and their prediction results were evaluated. The root mean square error was used as the performance indicator, and the learning results indicate that the random forest model outperforms the other models for both the training and testing sets. To examine the generalizability of the learning models, the models were verified in relation to data measured on three days: 15 February, 28 February, and 1 March 2019. A comparison between the model predicted and the measured data indicates that the random forest model provides the most stable and accurate prediction values and could clearly present the distribution of highly polluted areas. The results of these models are visualized in the form of maps by using a web application. The maps allow users to understand the distribution of polluted areas intuitively.

scholarly journals Predict multicategory causes of death in lung cancer patients using clinicopathologic factors

2020 ◽  
Author(s):  
Fei Deng ◽  
Haijun Zhou ◽  
Yong Lin ◽  
John Heim ◽  
Lanlan Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Cancer Patients ◽  
Causes Of Death ◽  
Random Forest Model ◽  
Multinomial Regression ◽  
Lung Cancer Patients ◽  
Forest Model ◽  
Clinicopathologic Factors

Background: Random forest model is a recently developed machine-learning algorithm, and superior to other machine learning and regression models for its classification function and better accuracy. But it is rarely used for predicting causes of death in lung cancer patients. On the other hand, specific causes of death in lung cancer patients are poorly classified or predicted, largely due to its categorical nature (versus binary death/survival). Methods: We therefore tuned and employed a random forest algorithm (Stata, version 15) to classify and predict specific causes of death in lung cancer patients, using the surveillance, epidemiology and end results-18 and several clinicopathological factors. The lung cancer diagnosed during 2004 were included for the completeness in their follow-up and death causes. The patients were randomly divided into training and validation sets (1:1 match). We also compared the accuracies of the final random forest and multinomial regression models. Results: We identified and randomly selected 40,000 lung cancers for the analyses, including 20,000 cases for either set. The causes of death were, in descending ranking order, were lung cancer (72.45 %), other causes or alive (14.38%), non-lung cancer (6.87%), cardiovascular disease (5.35%), and infection (0.95%). We found more 250 iterations and the 10 variables produced the best prediction, whose best accuracy was 69.8% (error-rate 30.2%). The final random forest model with 300 iterations and 10 variables reached an accuracy higher than that of multinomial regression model (69.8% vs 64.6%). The top-10 most important factors in the random-forest model were sex, chemotherapy status, age (65+ vs <65 years), radiotherapy status, nodal status, T category, histology type and laterality, which were also independently associated with 5-category causes of death. Conclusion: We optimized a random forest model of machine learning to predict the specific cause of death in lung cancer patients using a set of clinicopathologic factors. The model also appears more accurate than multinomial regression model.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

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