Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network (original) (raw)
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Intelligent Risk Alarm for Asthma Patients using Artificial Neural Networks
International Journal of Advanced Computer Science and Applications , 2020
Asthma is a chronic disease of the airways of the lungs. It results in inflammation and narrowing of the respiratory passages; which prevents air flow into the airways and leads to frequent bouts of shortness of breath with wheezing accompanied by coughing and phlegm after exposure to inhalation of substances that provoke allergic reactions or irritation of the respiratory system. Data mining in healthcare system is very important in diagnosing and understanding data, so data mining aims to solve basic problems in diagnosing diseases due to the complexity of diagnosing asthma. Predicting chemicals in the atmosphere is very important and one of the most difficult problems since the last century. In this paper, the impact of chemicals on asthma patient will be presented and discussed. Sensor system called MQ5 will be used to examine the smoke and nitrogen content in the atmosphere. MQ5 will be inserted in a wristwatch that checks the smoke and nitrogen content in the patient's place, the system shall issue a warning alarm if this gas affects the person with asthma. It will be based on the Artificial Neural Networks (ANN) algorithm that has been built using data that containing a set of chemicals such as carbon monoxide, NMHC (GT) acid gas, C6H6 (GT) Gasoline, NOx (GT) Nitrogen Oxide, and NO2 (GT) Nitrogen Dioxide. The temperature and humidity will be also used as they can negatively affect asthma patient. Finally, the rating model was evaluated and achieved 99.58% classification accuracy.
airccse.org
Machine Intelligence plays a crucial role in the design of expert systems in medical diagnosis. In India most of the people suffering from some sort of diseases like asthma, diabetics, cancer and many more. We consider the disease asthma for diagnosis. The diagnosis of asthma can be done in two ways 1) through questionnaire and 2) through clinical data. We considered both approaches to design the expert system for diagnosis of asthma. We have chosen some machine learning algorithms such as Context sensitive auto-associative memory neural network model (CSAMM), Backpropogation model, C4.5 algorithm, Bayesian Network, Particle Swarm Optimization [7]. We present a performance study on these algorithms in terms of accuracy and some outstanding characteristics.
International Journal on Soft Computing, 2011
Machine Intelligence plays a crucial role in the design of expert systems in medical diagnosis. In India most of the people suffering from some sort of diseases like asthma, diabetics, cancer and many more. We consider the disease asthma for diagnosis. The diagnosis of asthma can be done in two ways 1) through questionnaire and 2) through clinical data. We considered both approaches to design the expert system for diagnosis of asthma. We have chosen some machine learning algorithms such as Context sensitive auto-associative memory neural network model[1] (CSAMM), Backpropogation model, C4.5 algorithm, Bayesian Network, Particle Swarm Optimization [7]. We present a performance study on these algorithms in terms of accuracy and some outstanding characteristics.
An Artificial intelligence technique for the prediction of persistent asthma in children
Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, 2010
ABSTRACT The prediction of asthma that persists throughout childhood and into adulthood, in early life of a child has practical, clinical and prognostic implications and sets the basis for the future prevention. Artificial Neural Networks (ANNs) seems to be a superior tool for analyzing data sets where nonlinear relationships are existing between the input data and the predicted output. This study presents an effective machine-learning approach based on Multi-Layer Perceptron (MLP) neural networks, for the prediction of persistent asthma in children. Through a feature reduction, 10 high importance prognostic factors correlated to persistent asthma have been discovered. The feature selection approach results in 89.8% reduction of the initial number of features. Afterwards, a feature reduced classifier is constructed, which achieves 100% accuracy on the training and test data sets. Experimental results are presenting and verify this statement.
Intelligent Diagnosis of Asthma Using Machine Learning Algorithms
Data mining in healthcare is a very important field in diagnosis and in deeper understanding of medical data. Health data mining intends to solve real-world problems in diagnosing and treating diseases. One of the most important applications of data mining in the domain of machine learning is diagnosis, and this type of diagnosis of the disease asthma is a notable challenge due to the lack of sufficient knowledge of physicians concerning this disease and because of the complexity of asthma. The purpose of this research is the skillful diagnosis of asthma using efficient algorithms of machine learning. This study was conducted on a dataset consisting of 169 asthmatics and 85 non-asthmatics visiting the Imam Khomeini and MasseehDaneshvari Hospitals of Tehran. The algorithms of k – nearest neighbors , random forest , and support vector machine, together with pre – processing and efficient training were implemented on this dataset ,and the degrees of accuracy and specificity of the system used in our study were calculated compared with each other and with those of previous research. From among the different values for neighborhood, the highest degree of specificity was achieved with five neighbors. Our method was investigated together with other methods of machine learning and similar research, and the ROC curve was plotted, too. Other methods achieved suitable results as well, and they can be relied on. Therefore, we propose our approach based on the k-nearest algorithm together with pre-processing based on the Relief – F strategy and the Cross Fold data sampling as an efficient method in artificial intelligence with the purpose of data mining for the classification and differential diagnosis of diseases.
Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks
Advances in Artificial Intelligence, 2013
The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high...
Use of an artificial neural network in estimating prevalence and assessing underdiagnosis of asthma
Neural Computing & Applications, 1997
An artificial neural network was trained in the recognition of asthmatics in a general practice population, employing cross-validation on a subset of 350 patients of known asthmatic status. The trained network was then run on the data from 3139 patients whose asthmatic status was unknown. Using the values from the test set as estimates of sensitivity and specificity, the number predicted positive was adjusted to allow for false positives and false negatives to give an estimate of asthma prevalence and the minimum under-diagnosis rate that this suggested for the population. Using different data sets and network structures, prevalence rates of approximately 16-21% were measured providing evidence, even after allowing for maximum variablity in the estimates, consistent with under-diagnosis of at least a small percentage (0.7-4.0%). To provide a more precise estimate of the rate of this under-diagnosis and associated prevalence, a larger training and testing set of more accurately labelled cases is planned.
Effective asthma disease prediction using naive Bayes — Neural network fusion technique
2014 International Conference on Parallel, Distributed and Grid Computing, 2014
Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15-20 million people are suffering from asthma in India[1] .This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and neural network proved to be the best among classification algorithms in the diagnosis of asthma. This methodology is evaluated using 1024 raw data obtained from a city hospital. The proposed approach helps patients in their diagnosis of asthma.
Decision Support System for the Diagnosis of Asthma Severity Using Fuzzy Logic
2012
Asthma is a chronic inflammatory lung disease. Globally Asthma is major public health problem due to its incurable nature and misdiagnosis. In this research paper our work is concerned with the intelligent diagnosis of the severity of the Asthma disease. An automated system has been developed using a self-organizing fuzzy rule-based system. It utilizes the intrinsic ability to deal with the uncertainty and rejects the dealing of add-on mechanisms with imperfect data. Five symptoms have been taken (DSF (Day time symptoms frequency) and NSF (Night time symptoms frequency) PEFR (Peak Expiratory Flow Rate), PEFR variability and SaO2 (Saturation of oxygen) as input and one output for the decision of the asthmatic conditions. For designing of fuzzy inference system rule base play major role in its performance and fine tuning process optimizes the membership functions stored in the data base. The results of the manually constructed inference system was found to be correct when compared with the field data output.