An application of artificial neural network classifier for medical diagnosis

In recent year, various models have been proposed for medical diagnosis, which broadly can be classified into physical-based approaches and statistical-based approaches. Uncertainty and imprecision are the most important problems in medical diagnosis, other many problems in medical diagnostic domains need to be represented at varying degrees of diagnosis to be solved. Moreover, classification is very important in computer-aided medical diagnosis. In this respect, Artificial Neural Network (ANN) have been successfully applied and with no doubt, they provide the ability and potentials to diagnose the diseases. Therefore, this research focuses on using ANN to classify medical data. ANN model with two layers of tunable weights were used and trained using four different backpropagation algorithms while are the gradient descent(GD), gradient descent with momentum(GDM), gradient descent with adaptive learning rate(GDA) and gradient descent with momentum and adaptive learning rate(GDX). The network was used to classify three sets of medical data taken from UCI machine learning repository. The ability of all training algorithms tested and compared to each other on all datasets. Simulation results proved the ability of ANN for medical data classification with high accuracy and excellent performance and efficiency. This research provides the possibility of reduce costs and human resources. Increasing speed to find the results of medical analysis by using ANN also contributes in saving time for both physicians and patient

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

A Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data

The diagnosis of product defects is an important task in manufacturing, and machine learning-based approaches have attracted interest from both the industry and academia. A high-quality dataset is necessary to develop a machine learning model, but the manufacturing industry faces several data-collection issues including partially shuffled data, which arises when a product ID is not perfectly inferred and yields an unstable machine learning model. This paper introduces latent variables to formulate a supervised learning model that addresses the problem of partially shuffled data. The experimental results show that our graphical model deals with the shuffling of product order and can detect a defective product far more effectively than a model that ignores shuffling.This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1088255)

Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data

The diagnosis of product defects is an important task in manufacturing, and machine learning-based approaches have attracted interest from both the industry and academia. A high-quality dataset is necessary to develop a machine learning model, but the manufacturing industry faces several data-collection issues including partially shuffled data, which arises when a product ID is not perfectly inferred and yields an unstable machine learning model. This paper introduces latent variables to formulate a supervised learning model that addresses the problem of partially shuffled data. The experimental results show that our graphical model deals with the shuffling of product order and can detect a defective product far more effectively than a model that ignores shuffling.This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1088255)

Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization (TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature