Predicting disease outcomes using an artificial neural network.
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Predicting disease outcomes using an artificial neural network. by Amelia Smith

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Published by Oxford Brookes University in Oxford .
Written in English


Book details:

Edition Notes

Thesis (M.Sc.) - Oxford Brookes University, Oxford, 2003.

ContributionsMarshall, Pete., Oxford Brookes University. School of Technology. Department of Computing.
ID Numbers
Open LibraryOL15625121M

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  In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques. Procedures. We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson’s Progressive Marker Initiative (PPMI) by: 1. According to artificial neural network (ANN) outputs, patients were classified into 3 prognostic groups (good, intermediate, and poor prognosis) according to cut‐off points of the 25th and 50th percentiles for 1‐year survival prediction and the 50th and 75th percentiles for 5‐year survival prediction. Recently however, there has been growing interest in the use of artificial neural networks for prediction. The creation of a large database containing high quality data on renal transplantation patients in Wales offers an ideal opportunity to research a new area viz., the ability of these techniques to accurately predict outcomes such as the Cited by: This paper presents a modified artificial neural network (ANN) classifier technique with a MapReduce framework for the prediction of disease. For preprocessing, min–max normalization is carried.

  Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed.   This technique, inspired by the brain’s neural networks, uses multiple layers (hence ‘deep’) of non-linear processing units (analogous to ‘neurons’) to teach itself how to understand data and then to classify the record or make predictions. This new study is an example of deep learning applied to medical prediction tasks. Storm surge induced by severe typhoons has caused many catastrophic tragedies to coastal communities over past decades. Accurate and efficient prediction/assessment of storm surge is still an important task in order to achieve coastal disaster mitigation especially under the influence of climate change. This study revisits storm surge predictions using artificial neural networks (ANN) and. Aims: The aim of this paper is to present techniques indicators of artificial neural networks (ANNs) model using for predicting the exact movements of stock price in the daily Libyan Stock Market (LSM) index forecasting. Study Design: Research paper. Place and Duration of Study: Libyan stock market from January 2, to Ma Methodology: The data from an emerging market Libyan.

Purpose. Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition. Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data by Ma’mon M. Hatmal 1,*, Salim M. Abderrahman 2, Wajeha Nimer 2, Zaynab Al-Eisawi 1, Hamzeh J. Al-Ameer 3, Mohammad A. I. Al-Hatamleh 4, Rohimah Mohamud 4,5 and Walhan Alshaer 6,*. Request PDF | Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction | Predicting the outcome of a medical procedure or .   Prediction of pelvic organ prolapse using an artificial neural network. Am J Obstet Gynecol This allocation algorithm has been suggested as providing optimal training of the ANN by our laboratory and prior authors. 11, 12 The allocation of patients among the 3 groups was performed at random using a computerized randomization algorithm in.