scholarly journals Stroke Classification Model using Logistic Regression

2021 ◽  
Vol 2123 (1) ◽  
pp. 012016
Author(s):  
S Annas ◽  
A Aswi ◽  
M Abdy ◽  
B Poerwanto
Keyword(s):  
Logistic Regression ◽  
Length Of Stay ◽  
Total Cholesterol ◽  
Blood Sugar Level ◽  
Hemorrhagic Stroke ◽  
Classification Model ◽  
Response Variable ◽  
Modeling Accuracy ◽  
And Gender

Abstract This study aims to determine the factors that significantly affect the classification of stroke. The response variable used is the type of stroke, namely non-hemorrhagic stroke and hemorrhagic stroke. The predictors used were cholesterol level, blood sugar level, temperature, length of stay, pulse rate, and gender. By using logistic regression, the results obtained modeling accuracy of 74.8% where the predictors that have a significant effect (alpha <0.05) are total cholesterol and length of stay.

Classification of Surgical Complications

2017 ◽  
Author(s):  
Andrew Gonzalez ◽  
SreyRam Kuy
Keyword(s):  
Length Of Stay ◽  
Study Design ◽  
Surgical Complications ◽  
Clinical Case ◽  
Classification Model ◽  
Design Study ◽  
Study Intervention

This landmark paper proposed a graded classification model for surgical complications and determined that there is a direct correlation between complication grade and patient length of stay. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case.

scholarly journals CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

2021 ◽  
Vol 12 ◽  
Author(s):  
Osamah Alwalid ◽  
Xi Long ◽  
Mingfei Xie ◽  
Jiehua Yang ◽  
Chunyuan Cen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intracranial Aneurysm ◽  
Regression Model ◽  
Ct Angiography ◽  
Logistic Regression Model ◽  
Aneurysm Rupture ◽  
Classification Model ◽  
High Morbidity ◽  
Unruptured Aneurysms

Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p &lt; 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p &lt; 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

scholarly journals Lama Hari Rawat Pasien Stroke

2020 ◽  
Vol 9 (2) ◽  
pp. 117-122
Author(s):  
Novita Nirmalasari ◽  
Muhamat Nofiyanto ◽  
Rizqi Wahyu Hidayati
Keyword(s):  
Length Of Stay ◽  
Hemorrhagic Stroke ◽  
Treatment Process ◽  
Clinical Signs ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Cerebral Function ◽  
Stroke Patients ◽  
Design Data

Background: Stroke has remained the leading cause of death globally in the last 15 years. Stroke is rapidly developing clinical signs of focal or global disturbance of cerebral function. Hospitalization is a treatment process which including patient to stay at the hospital. Length of stay is influenced by the type of stroke.  This  study aimed to know classification of stroke and length of stay of stroke patients. Methods: The study was a descriptive study with restrospective design. Data collected from medical record from May until December 2020 in RS PKU Muhammadiyah Yogyakarta. There were 207 patients with stroke. Descriptive data is then processed. Results: The results showed the highest percentage stroke patients male (50,24%), non hemorrhagic stroke (57,49%), length of stay hemorrhagic stroke 8 days. Conclusion: The result of this study may provide nursing research in patients with stroke.

scholarly journals On Classification of All-rounders of the Indian Premier League (IPL): A Bayesian Approach

2011 ◽  
Vol 36 (4) ◽  
pp. 51-66 ◽  
Author(s):  
Hemanta Saikia ◽  
Dibyojyoti Bhattacharjee
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Training Sample ◽  
Bayesian Classification ◽  
Classification Model ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Key Points ◽  
Overlapping Classes

An all-rounder can take an imperative role in any version of the game of cricket, whether it is a test match or any other limited-over format of the game. The study classifies the performance of all-rounders who participated in IPL based on their strike rate and economy rate. Based on the factors mentioned, the all-rounders can be divided into four non-overlapping classes, viz., Performer, Batting All-rounder, Bowling All-rounder, and Under-performer. Several predictor variables that are supposed to influence the performance of all-rounders are considered. Step-wise multinomial logistic regression (SMLR) is used to identify the significant predictors. Samples of six incumbent all-rounders who had not participated in the first three seasons of IPL are considered. The significant predictors were then used to predict the expected class of an incumbent all-rounder using naive Bayesian classification model. The relevant data were collected from the websites, www.cricinfo.org and www.cricketnirvana.com. The key points of this study are as follows: The training sample is populated with 35 all-rounders who had performed in the first three seasons of IPL. Two variables, viz., strike rate (number of runs scored per 100 balls faced) and economy rate (average number of runs scored per over against the bowler) are used to classify the all-rounders as follows: Performer: An all-rounder with strike rate above median and economy rate below median. Batting All-rounder: An all-rounder with strike rate above median and economy rate above median. Bowling All-rounder: An all-rounder with strike rate below median and economy rate below median. Under-performer: An all-rounder with strike rate below median and economy rate above median. The step-wise multinomial logistic regression (SMLR) was used to identify the significant variables that are actually responsible for classification of the all-rounders. The strike rate in ODI, strike rate in Twenty-20, economy rate in ODI, economy rate in Twenty-20 and bowling type (Spin or Fast) of the all-rounders are found to be significant in determining the class of an all-rounder. The naive Bayesian classification model is used for forecasting the expected class of allrounders based on the significant predictors for six incumbent all-rounders who had played only in fourth season of IPL. The prediction done before IPL IV was then compared with the actual situation at the end of the tournament. It is found that four predictions were performed correctly out of the six. This model would be useful for the participating teams' management while deciding the bid of an all-rounder in the upcoming season of IPL as per their requirement.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anping Guo ◽  
Jin Lu ◽  
Haizhu Tan ◽  
Zejian Kuang ◽  
Ying Luo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Liver Disease ◽  
Length Of Stay ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospital Length ◽  
Clinical Treatment ◽  
Hospital Length Of Stay ◽  
Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

scholarly journals CNN-Based Acoustic Scene Classification System

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 371
Author(s):  
Yerin Lee ◽  
Soyoung Lim ◽  
Il-Youp Kwak
Keyword(s):  
Low Complexity ◽  
Classification Model ◽  
Weight Class ◽  
Scene Classification ◽  
Audio File ◽  
General Classification ◽  
Team Task ◽  
Real World Applications ◽  
Activation Mapping

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.

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