scholarly journals Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Kindie Biredagn Nahato ◽  
Khanna Nehemiah Harichandran ◽  
Kannan Arputharaj
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Heart Disease ◽  
Missing Values ◽  
Classification Model ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Knowledge Mining ◽  
Clinical Dataset ◽  
Two Stages

The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

scholarly journals Analysis of evaluation problems of the risk situation of patieents suffering from ischemic heart disease

2012 ◽  
Vol 60 (2) ◽  
pp. 125-134 ◽  
Author(s):  
Vladimír Konečný ◽  
Milan Sepši ◽  
Oldřich Trenz
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Complex Analysis ◽  
Partial Solution ◽  
Health Condition ◽  
Ischemic Heart ◽  
Classification Model ◽  
Data Set ◽  
Software Application

The ischemic heart disease represents a very common health issue which, thanks to its seriousness, impacts a big part of the population and is the cause of about one third of all death cases in the Czech Republic. For the analysis itself, data from medicinal practice of one of the authors of the article have been used and this study is a follow up of his PhD thesis. Concretely it was a set of patients which were being rehabilitated after a heart stroke; the results of the medical examination of these patients create 26 parameters. This data has been obtained in the course of the patients’ treatment. In the first phase of generating the classification model, the parameters that didn’t have a detrimental effect on the assessment of health condition of the patients have been removed from the data set and have been kept in the category of additional parameters. For the classification itself, an approach from artificial intelligence – applying a neural network - has been chosen. For the recording and transformation of the entering data a special application has been made. The classification and analysis of the data is performed on an experimental model of the self-learning of a neural network. The conclusions that arise from the initial analysis of this issue and the partial solution can be generalized and when using an appropriate software application they could even be used in medical practice. To do a complex analysis of the influence of all 26 parameters on the overall state of health of the patients is very difficult. A decision-making model appears to be a good solution. Last but not least, the proposed solution has to be verified on a bigger sample of patients afflicted by the ischemic heart disease.

scholarly journals Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
V. R. Elgin Christo ◽  
H. Khanna Nehemiah ◽  
B. Minu ◽  
A. Kannan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Clinical Diagnosis ◽  
Missing Values ◽  
Clinical Decision Making ◽  
Fitness Function ◽  
Breast Cancer Dataset ◽  
Backpropagation Neural Network ◽  
Bioinspired Algorithms

A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocessing, feature selection, and classification. Hot deck imputation has been used for handling missing values and min-max normalization is used for data transformation. Wrapper approach that employs bioinspired algorithms, namely, Differential Evolution, Lion Optimization, and Glowworm Swarm Optimization with accuracy of AdaBoostSVM classifier as fitness function has been used for feature selection. Each bioinspired algorithm selects a subset of features yielding three feature subsets. Correlation-based ensemble feature selection is performed to select the optimal features from the three feature subsets. The optimal features selected through correlation-based ensemble feature selection are used to train a gradient descendant backpropagation neural network. Ten-fold cross-validation technique has been used to train and test the performance of the classifier. Hepatitis dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine (UCI) Machine Learning repository have been used to evaluate the classification accuracy. An accuracy of 98.47% is obtained for Wisconsin Diagnostic Breast Cancer dataset, and 95.51% is obtained for Hepatitis dataset. The proposed framework can be tailored to develop clinical decision-making systems for any health disorders to assist physicians in clinical diagnosis.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

Backpropagation neural network for processing of missing data in breast cancer detection

IRBM ◽  
2021 ◽  
Author(s):  
L. Zhang ◽  
Hongyan Cui ◽  
Bingqing Liu ◽  
Chao Zhang ◽  
Berthold K.P. Horn
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Missing Data ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Backpropagation Neural Network

HIOC: a hybrid imputation method to predict missing values in medical datasets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooja Rani ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Breast Cancer ◽  
Heart Disease ◽  
Missing Values ◽  
Imputation Method ◽  
Support Vector ◽  
Correct Prediction ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Content Type ◽  
Imputation Methods

PurposeDecision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.Design/methodology/approachThe proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.FindingsThe results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.Originality/valueThe proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

Fuzzy Systems ◽  
2017 ◽  
pp. 682-714 ◽  
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

2017 ◽  
pp. 293-325
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

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