723 ARTIFICIAL NEURAL NETWORK (ANN) TO PREDICT CIRRHOSIS IN CHRONIC HEPATITIS C (CHC). COMPARISON WITH A LOGISTIC REGRESSION (LG) MODEL

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

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TXT-tool 1.081-6.1 A Comparative Study of the Binary Logistic Regression (BLR) and Artificial Neural Network (ANN) Models for GIS-Based Spatial Predicting Landslides at a Regional Scale

Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools ◽

10.1007/978-3-319-57774-6_10 ◽

2017 ◽

pp. 139-151 ◽

Cited By ~ 13

Author(s):

Jie Dou ◽

Hiromitsu Yamagishi ◽

Zhongfan Zhu ◽

Ali P. Yunus ◽

Chi Wen Chen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Comparative Study ◽

Regional Scale ◽

Binary Logistic Regression ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors

Journal of Viral Hepatitis ◽

10.1046/j.1365-2893.1998.00108.x ◽

1998 ◽

Vol 5 (4) ◽

pp. 255-264 ◽

Cited By ~ 20

Author(s):

G. H. Haydon ◽

R. Jalan ◽

M. Ala‐Korpela ◽

Y. Hiltunen ◽

J. Hanley ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Chronic Hepatitis ◽

Hepatitis C ◽

Network Analysis ◽

Chronic Hepatitis C ◽

Neural Network Analysis ◽

Clinical Factors ◽

Hepatitis C Infection ◽

Artificial Neural Network Analysis

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M1527: Prediction of Barrett's Esophagus (BE) in Patients With Gastroesophageal Reflux Disease (GERD) Using Logistic Regression Model (LRM) and Artificial Neural Network (ANN)

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2010.03.527 ◽

2010 ◽

Vol 71 (5) ◽

pp. AB245

Author(s):

Srinivas Gaddam ◽

Amit Rastogi ◽

Neil Gupta ◽

Sachin B. Wani ◽

Ajay Bansal ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Gastroesophageal Reflux Disease ◽

Gastroesophageal Reflux ◽

Regression Model ◽

Reflux Disease ◽

Logistic Regression Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Artificial Neural Network Model for Hepatitis C Stage Detection

10.46603/ejcee.v1i1.6 ◽

2020 ◽

Vol 1 (1) ◽

pp. 11-16

Author(s):

Dhiman Sarma ◽

◽

Tanni Mittra ◽

Muntasir Hoq ◽

Promila Haque ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Chronic Hepatitis ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Infected People ◽

Egyptian Patients ◽

Artificial Neural ◽

Acute And Chronic Hepatitis ◽

Prediction Evaluation

Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). In 2015, WHO reports that 71 million people were living with HCV, and 1.34 million died. In 2017, 13.1 million infected people knew their diagnosis and around 5 million patients were treated. HCV can cause acute and chronic hepatitis, where 20% of chronic hepatitis progresses to final-stage chronic liver cancer. Currently, no vaccine of HCV exists, and no effective treatments are available for demolishing the progression of hepatitis C. So spotting the stages of the disease is essential for diagnostic and therapeutic management of infected patients. This paper attempts to detect stages of hepatitis C virus so that further diagnosis and medication of hepatitis patients can be prescribed. It uses a supervised artificial neural network to make a prediction. Evaluation of results is done by cross-validation using the holdout method. Hepatitis C Egyptian-patients' dataset from UCI Machine Learning Repository is used for feeding the algorithms. The research succeeds to detect the hepatitis C stages and achieves an accuracy of 97%.

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The Validity of Multinomial Logistic Regression and Artificial Neural Network in Predicting Sukuk Rating: Evidence from Indonesian Stock Exchange

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091520500320 ◽

2020 ◽

Vol 23 (04) ◽

pp. 2050032

Author(s):

Muhammad Luqman Nurhakim ◽

Zainul Kisman ◽

Faizah Syihab

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Predictive Accuracy ◽

Stock Exchange ◽

Multinomial Logistic Regression ◽

Global Market ◽

Analysis Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

The Sukuk (shariah bond) market is developing in Indonesia and potentially will capture the global market in the future. It is an attractive investment product and a hot current issue in the capital market. Especially, the problem of predicting an accurate and trustworthy rating. As the Sukuk market developed, the issue of Sukuk rating emerged. As ordinary investors will have difficulty predicting their ratings going forward, this research will provide solutions to the problems above. The objective of this study is to determine the Indonesian Sukuk rating determinants and comparing the Sukuk rating predictive model. This research uses Artificial Neural Network (ANN) and Multinomial Logistic Regression (MLR) as the predictive analysis model. Data in this study are collected by purposive sampling and employing Sukuk rated by PEFINDO, an Indonesian rating agency. Findings in this study are debt, profitability and firm size significantly affecting Sukuk rating category and the ANN performs better predictive accuracy than MLR. The implications of the results of the research for the issuer and bondholder are a higher level of credit enhancement, a higher level of profitability, and the bigger size of firm rewarding higher Sukuk rating.

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Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel

JISKA (Jurnal Informatika Sunan Kalijaga) ◽

10.14421/jiska.2021.61-04 ◽

2021 ◽

Vol 6 (1) ◽

pp. 29

Author(s):

Moch Shandy Tsalasa Putra ◽

Yufis Azhar

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Logistic Regression ◽

Management Systems ◽

Complex Data ◽

Hotel Performance ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Modern Era

Prediction for canceled booking hotels is an important part of hotel revenue management systems in the modern era. Because the predicted result can be used for the optimization of hotel performance. The application of machine learning will be very helpful for predicting canceled booking hotels because machine learning can process complex data. In this research, the proposed methods are Artificial Neural Network (ANN) and Logistic Regression. Later it will be done five times experiments with hyperparameter tuning to see which method is the most optimal to do prediction canceled booking hotel. From five times experiments, experiments number five (logistic regression with GridSearchCV) is the most optimal for predicting canceled booking hotels, with 79.77% accuracy, 85.86% precision, and 55.07% recall.

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