Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification.

Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.

Functional link convolutional neural network for the classification of diabetes mellitus.

Diabetes is a faction of metabolic ailments distinguished by hyperglycemia which is the consequence of a defect, in the action of insulin, insulin secretion, or both and producing various abnormalities in the human body. In recent years, the utilization of intelligent systems has been expanded in disease classification and numerous researches have been proposed. In this research article, a variant of Convolutional Neural Network (CNN) that is, Functional Link Convolutional Neural Network (FLCNN) is proposed for the diabetes classification. The main goal of this article is to find the potential of a computationally less complex deep learning network like FLCNN and applied the proposed technique on a real dataset of diabetes for classification. This article also presents the comparative studies where various other machine learning techniques are implemented and outcomes are compared with the proposed FLCNN network. The performance of each classification techniques have been evaluated based on standard measures and also validated with a non-parametric statistical test such as Friedman. Data for modeling diabetes classification is collected from Bombay Medical Hall, Upper Bazar, Ranchi, India. Accuracy achieve by the proposed classifier is more than 90% which is closer to the other state-of-the-art implemented classifiers.