scholarly journals Content Controlled Spectral Indices for Detection of Hydrothermal Alteration Minerals Based on Machine Learning and Lasso-Logistic Regression Analysis

2021 ◽  
Vol 14 ◽  
pp. 7435-7447
Author(s):  
Kyuhun Shim ◽  
Jaehyung Yu ◽  
Lei Wang ◽  
Sangin Lee ◽  
Sang-Mo Koh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Hydrothermal Alteration ◽  
Logistic Regression Analysis ◽  
Spectral Indices ◽  
Alteration Minerals

scholarly journals Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

2018 ◽  
Vol 7 (11) ◽  
pp. 428 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Soo Yoon ◽  
Seong-Mi Yang ◽  
Won Kim ◽  
Ho-Geol Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Gradient Boosting Machine

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

scholarly journals Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0221911 ◽  
Author(s):  
Shinya Suzuki ◽  
Takeshi Yamashita ◽  
Tsuyoshi Sakama ◽  
Takuto Arita ◽  
Naoharu Yagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Cardiovascular Events ◽  
Risk Models

scholarly journals Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shuanbao Yu ◽  
Jin Tao ◽  
Biao Dong ◽  
Yafeng Fan ◽  
Haopeng Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Diagnostic Accuracy ◽  
Gleason Score ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Learning Models ◽  
Net Benefit ◽  
Machine Learning Models

Abstract Background Machine learning has many attractive theoretic properties, specifically, the ability to handle non predefined relations. Additionally, studies have validated the clinical utility of mpMRI for the detection and localization of CSPCa (Gleason score ≥ 3 + 4). In this study, we sought to develop and compare machine-learning models incorporating mpMRI parameters with traditional logistic regression analysis for prediction of PCa (Gleason score ≥ 3 + 3) and CSPCa on initial biopsy. Methods A total of 688 patients with no prior prostate cancer diagnosis and tPSA ≤ 50 ng/ml, who underwent mpMRI and prostate biopsy were included between 2016 and 2020. We used four supervised machine-learning algorithms in a hypothesis-free manner to build models to predict PCa and CSPCa. The machine-learning models were compared to the logistic regression analysis using AUC, calibration plot, and decision curve analysis. Results The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) yielded similar diagnostic accuracy with logistic regression, while classification and regression tree (CART, AUC = 0.834 and 0.867) had significantly lower diagnostic accuracy than logistic regression (AUC = 0.894 and 0.917) in prediction of PCa and CSPCa (all P < 0.05). However, the CART illustrated best calibration for PCa (SSR = 0.027) and CSPCa (SSR = 0.033). The ANN, SVM, RF, and LR for PCa had higher net benefit than CART across the threshold probabilities above 5%, and the five models for CSPCa displayed similar net benefit across the threshold probabilities below 40%. The RF (53% and 57%, respectively) and SVM (52% and 55%, respectively) for PCa and CSPCa spared more unnecessary biopsies than logistic regression (35% and 47%, respectively) at 95% sensitivity for detection of CSPCa. Conclusion Machine-learning models (SVM and RF) yielded similar diagnostic accuracy and net benefit, while spared more biopsies at 95% sensitivity for detection of CSPCa, compared with logistic regression. However, no method achieved desired performance. All methods should continue to be explored and used in complementary ways.

scholarly journals Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

2018 ◽  
Vol 7 (10) ◽  
pp. 322 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Hyun-Kyu Yoon ◽  
Karam Nam ◽  
Youn Cho ◽  
Tae Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches

Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data were obtained. The primary outcome was postoperative acute kidney injury (AKI) defined according to the Kidney Disease Improving Global Outcomes criteria. The following machine learning techniques were used: decision tree, random forest, extreme gradient boosting, support vector machine, neural network classifier, and deep learning. The performance of these techniques was compared with that of logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUC). During the first postoperative week, AKI occurred in 770 patients (38.3%). The best performance regarding AUC was achieved by the gradient boosting machine to predict the AKI of all stages (0.78, 95% confidence interval (CI) 0.75–0.80) or stage 2 or 3 AKI. The AUC of logistic regression analysis was 0.69 (95% CI 0.66–0.72). Decision tree, random forest, and support vector machine showed similar performance to logistic regression. In our comprehensive comparison of machine learning approaches with logistic regression analysis, gradient boosting technique showed the best performance with the highest AUC and lower error rate. We developed an Internet–based risk estimator which could be used for real-time processing of patient data to estimate the risk of AKI at the end of surgery.

ANALISIS HUBUNGAN SARANA, PENGETAHUAN, DAN SIKAP BIDAN PRAKTIK MANDIRI DENGAN KEPATUHAN MENJALANKAN STANDAR PELAYANAN ANTENATAL CARE DI KOTA PADANGSIDIMPUAN

2019 ◽  
Vol 2 (1) ◽  
pp. 27-33
Author(s):  
Megawati Sinambela ◽  
Evi Erianty Hasibuan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Logistic Regression Analysis ◽  
Care Service ◽  
Multivariate Logistic Regression Analysis ◽  
Bivariate Analysis ◽  
Cross Sectional ◽  
The North

Antenatal care is a service provided to pregnant women to monitor, support maternal health and detect mothers whether normal or problematic pregnant women. According to the WHO, globally more than 70% of maternal deaths are caused by complications of pregnancy and childbirth such as hemorrhage, hypertension, sepsis, and abortion. Based on data obtained from the profile of the North Sumatra provincial health office in 2017, in the city of Padangsidimpuan in 2017 the coverage of ANC visits reached (76.58%) and had not reached the target in accordance with the 2017 Provincial Health Office strategy plan (95%). This type of research was an observational analytic study with a cross sectional design. The population in this study were independent practice midwives who were in the Padangsidimpuan, the sample in this study amounted to 102 respondents. The technique of collecting data used questionnaires and data analysis used univariate, bivariate and multivariate analysis with logistic regression analysis. Based on bivariate analysis showed that there was a relationship between facilities, knowledge and attitudes of independent midwives with compliance with the standards of antenatal care services with a value of p <0.05. The results of the study with multivariate logistic regression analysis showed that the factors associated with the compliance of independent midwives in carrying out antenatal care service standards were attitudes with values (p = 0.026).

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