Search

Breast cancer is the most prevalent cancer affecting women. Identifying breast cancer risk factors are crucially to be established. The main objectives of the present study were to identify the predictors of breast cancer risk factors and their relative importance using neural network analysis. The present study depended on neural network analysis of data posted on [1]. The dataset is about predictors of breast cancer. There were 9 covariates included and one dependent variable, output (no disease (1), or disease (2)). The dataset was composed of 116 cases. The category of no disease comprised 79 (68.1%) cases, whereas the disease category included 37 cases (31.9%). Architecture model was built with some characteristics such as training part: gross entropy error was 23.7884, the percent of incorrect predictions was 10.1%. Stopping rule used was 1 consecutive step(s) with no decrease in error. For testing part, gross entropy error was 14,327, the percent of incorrect predictions was 13.5%. The relative importance of breast cancer was in the following order: glucose, resistin, BMI, age, leptin, adiponectin, MCP-1, insulin, and HOMA. Taken together, neural network analysis is an efficient tool to predict breast cancer risk factors.

Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance. Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases. Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking. Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results. Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.