Prioritization of Schizophrenia Risk Genes by a Network-Regularized Logistic Regression Method

2016 ◽  
pp. 434-445 ◽  
Author(s):  
Wen Zhang ◽  
Jhin-Rong Lin ◽  
Rubén Nogales-Cadenas ◽  
Quanwei Zhang ◽  
Ying Cai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Method ◽  
Risk Genes ◽  
Logistic Regression Method

scholarly journals Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 42-55
Author(s):  
Radius Tanone ◽  
Arnold B Emmanuel
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Learning Approach ◽  
Know How ◽  
Independent Variables ◽  
Machine Learning Approach ◽  
Logistic Regression Method ◽  
Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  

scholarly journals Talent Performance Analysis Using People Analytics Approach

2021 ◽  
Vol 4 (1) ◽  
pp. 216-230
Author(s):  
Fahreza Nasril ◽  
Dian Indiyati ◽  
Gadang Ramantoko
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Research Question ◽  
Development Program ◽  
Individual Performance ◽  
Study Data ◽  
Regression Method ◽  
Process Data ◽  
Logistic Regression Method ◽  
The Right

The purpose of this study was to answer the research question "How is the prediction of Talent Performance in the following year with the application of People Analytics?" and knowing the description of employees who are potential talents, the resulting performance contributions, to the description of the development and retention efforts needed by Talent in order to be able to maintain their future performance and position as Talents compared to the previous People Analytics method using predictive analysis, namely prediction of Talent Performance in the year next. In this study, data analysis using the Multivariate Logistic Regression method is used to get the Prediction of the Performance of Talents who become the object of research in the form of individual performance quickly and precisely in accordance with the patterns drawn by individual Performance score data in previous years. And can provide insight regarding the projected strategies that need to be done to maintain the improvement of individual talent performance in the years of the assessment period. It also helps management in making decisions about the right Talent development program and determining which Talents are priorities. The population in this study were the talents of employees of PT. Angkasa Pura II (Persero) with a managerial level consisting of: Senior Leader, Middle Leader, and First Line Leader who has a Person Grade (PG) range of 13 to 21. The sample used is Middle Leader level talent with specified criteria and through a process data cleansing. The results of this study indicate that the variable that significantly affects the performance of the following year is the performance of the previous 2 years. Then prediction analysis can be done using these independent variables with the Multinomial Logistic Regression method, and to get prediction results with better accuracy can be done by the Random Forest method.

scholarly journals Prognostic algorithms for the progression of chronic heart failure depending on the clinical phenotype

2019 ◽  
Vol 18 (3) ◽  
pp. 41-47
Author(s):  
E. A. Polunina ◽  
L. P. Voronina ◽  
E. A. Popov ◽  
I. S. Belyakova ◽  
O. S. Polunina ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Chronic Heart Failure ◽  
Clinical Phenotype ◽  
Left Ventricular ◽  
Regression Method ◽  
Left Ventricular Ejection ◽  
Control Group ◽  
Ventricular Ejection Fraction ◽  
Logistic Regression Method

Aim. To develop a mathematical equation (algorithm) to predict the development of chronic heart failure (CHF) for three years, depending on the clinical phenotype.Material and methods. Three hundred forty five patients with CHF with a different left ventricular ejection fraction (preserved, mean, low) were examined. The control group included somatically healthy individuals (n=60). In all patients, 48 parameters that most widely characterize the pathogenesis of CHF (gender-anamnestic, clinical, instrumental, biochemical) were analyzed. To isolate phenotypes, dispersive and cluster analysis was used: the hierarchical classification method and the k-means method. In the development of algorithms we used binary logistic regression method. We used ROC curve to assess the quality of the obtained algorithms.Results. We identified four phenotypes in patients with CHF: fibro-rigid, fibro-inflammatory, inflammatory-destructive, dilated-maladaptive. For the first three phenotypes, a mathematical logistic regression method was used to develop mathematical models for predicting the progression of CHF for three years, with the release of predictors for each phenotype. Belonging to the dilatedmaladaptive phenotype according to the results of the analysis is already an indicator of an unfavorable prognosis in patients with CHF.Conclusion. The developed algorithms based on the selected phenotypes have high diagnostic sensitivity and specificity and can be recommended for use in clinical practice.

Effects of a Bolster Curvature on an Automobile Seat Fit

2021 ◽  
Vol 65 (1) ◽  
pp. 423-426
Author(s):  
Baekhee Lee ◽  
Byoung-Keon (Daniel) Park ◽  
Kihyo Jung ◽  
Jangwoon Park
Keyword(s):  
Logistic Regression ◽  
Design Process ◽  
Cross Sections ◽  
Classification Accuracy ◽  
Regression Method ◽  
Digital Design ◽  
Cross Sectional ◽  
Development Costs ◽  
Logistic Regression Method ◽  
Vehicle Seat

Vehicle-seat dimensions measured at specific cross-sections have been historically utilized as shape determinants to evaluate a driver’s seat fit. The present study is intended to quantify the relationships between seat fits and the seat dimensions for designing an ergonomic vehicle seat. Eight seat engineers evaluated seat fits for 54 different driver seats based on their expertise. Five seat dimensions were measured at six cross-sectional planes using a custom-built, computerized program. The best-subset-logistic-regression method was employed to model the relationships between the seat fit and the seat dimensions. As a result, significant seat dimensions, such as insert width, bolster height, and/or bolster curvature, on the subjective seat fit (e.g., loose-fit, right-fit, and tight-fit) were quantified. The developed models showed 98% overall classification accuracy throughout the cross-sectional planes. The models promote a digital design process of an automobile seat, which would increase the efficiency of the process and reduce the development costs.

Modeling Species Distribution

2011 ◽  
pp. 171-197 ◽  
Author(s):  
Yongyut Trisurat ◽  
Albertus G. Toxopeus
Keyword(s):  
Logistic Regression ◽  
Maximum Entropy ◽  
Habitat Suitability ◽  
Logistic Regression Model ◽  
Entropy Method ◽  
Regression Method ◽  
Logistic Regression Method ◽  
Suitable Habitats ◽  
Suitable Area ◽  
Available Information

The results show that among the three approaches, the potentially suitable habitats derived from cartographic overlay cover the largest area and are likely to overestimate existing occurrence areas. The logistic regression model predicts approximately 56% as suitable area, while maximum entropy results covers approximately 9% of the sanctuary. Although the results show large differences in the suitable areas, it should not be concluded that any one method always proves better than the others. Utilization of any method is dependent on the situation and available information. If species observations are limited, the cartographic overlay or habitat suitability is recommended. The logistic regression method is recommended when adequate presence and absence data are available. If presence-only data is available, a niche-based model or the maximum entropy method (MAXENT) is highly recommended.

scholarly journals Predicting Breast Cancer Using Logistic Regression and Multi-Class Classifiers

2018 ◽  
Vol 7 (4.20) ◽  
pp. 22 ◽  
Author(s):  
Jabeen Sultana ◽  
Abdul Khader Jilani ◽  
. .
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Breast Cancer Dataset ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Logistic Regression Method ◽  
Simple Logistic

The primary identification and prediction of type of the cancer ought to develop a compulsion in cancer study, in order to assist and supervise the patients. The significance of classifying cancer patients into high or low risk clusters needs commanded many investigation teams, from the biomedical and the bioinformatics area, to learn and analyze the application of machine learning (ML) approaches. Logistic Regression method and Multi-classifiers has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. This paper explores the different data mining approaches using Classification which can be applied on Breast Cancer data to build deep predictions. Besides this, this study predicts the best Model yielding high performance by evaluating dataset on various classifiers. In this paper Breast cancer dataset is collected from the UCI machine learning repository has 569 instances with 31 attributes. Data set is pre-processed first and fed to various classifiers like Simple Logistic-regression method, IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers and REP Tree.  10-fold cross validation is applied, training is performed so that new Models are developed and tested. The results obtained are evaluated on various parameters like Accuracy, RMSE Error, Sensitivity, Specificity, F-Measure, ROC Curve Area and Kappa statistic and time taken to build the model. Result analysis reveals that among all the classifiers Simple Logistic Regression yields the deep predictions and obtains the best model yielding high and accurate results followed by other methods IBK: Nearest Neighbor Classifier, K-Star: instance-based Classifier, MLP- Neural network. Other Methods obtained less accuracy in comparison with Logistic regression method.  

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