Application of Neural Networks in Diagnosing Cancer Disease using Demographic Data

Abstract

Artificial Neural Network is a branch of Artificial intelligence, has been accepted as a new technology in computer science. Neural Networks are currently a 'hot' research area in medicine, particularly in the fields of radiology, urology, cardiology, oncology and etc. It has a huge application in many areas such as education, business; medical, engineering and manufacturing .Neural Network plays an important role in a decision support system. In this paper, an attempt has been made to make use of neural networks in the medical field (carcinogenesis (pre-clinical study)). In carcinogenesis, artificial neural networks have been successfully applied to the problems in both pre-clinical and post-clinical diagnosis. The main aim of research in medical diagnostics is to develop more cost-effective and easy-to-use systems, procedures and methods for supporting clinicians. It has been used to analyze demographic data from lung cancer patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department. For the lung cancer diagnosis problem, the concise rules extracted from the network achieve an high accuracy rate of on the training data set and on the test data set.

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

“…We have evaluated the prediction of machine learning algorithms, and obtained a very high accuracy rate. Machine learning has been used for prediction and diagnosis of several diseases, e.g., Parkinson's [9], cancer [10] and heart disease [11]. Among machine learning methods, Support Vector Machines (SVM) [12] have been used in malaria incidence prediction [13]; but this study has several shortcomings: (i) the dataset used was extremely small (the size is only 33), which makes accuracy of prediction questionable; (ii) the dataset was used without analysing ecological factors, which could result in the inclusion of statistically insignificant variables in the prediction model, and hence could cause overfitting; (iii) there is no systematic methodology to transform this predictor into a smart healthcare system.…”

Non-Contact Measurement of Blood Glucose based on Artificial Neural Network

“…Artificial Neural Network (ANN) is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation [4][5][6]. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.…”

“…The system based on temperature differences between two points on the ear (tragus and anti-helix) [3] that measured by a thermopile. This method supported by artificial neural network (ANN) computing system that has high accuracy and precision [4][5][6]. Fig.…”

Study on the Efficiency of a Multi-layer Perceptron Neural Network Based on the Number of Hidden Layers and Nodes for Diagnosing Coronary- Artery Disease

“…The MLP network with one or more hidden layers is renowned globally as a predictive program (9). The global predictive theory informs neural networks that any continuous function that maps the same range of cardinal values in the range of its cardinal value output can only be predicted accurately using an MLP with one hidden layer (10). used to increase the speed of convergence while no further hidden layers are required.…”