scholarly journals Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ashir Javeed ◽  
Sanam Shahla Rizvi ◽  
Shijie Zhou ◽  
Rabia Riaz ◽  
Shafqat Ullah Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Heart Disease ◽  
Prediction Accuracy ◽  
Feature Selection Method ◽  
Disease Diagnosis ◽  
Selection Method ◽  
Diagnostic Systems ◽  
Operating Characteristics ◽  
Heart Disease Diagnosis

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

scholarly journals Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dengqing Zhang ◽  
Yunyi Chen ◽  
Yuxuan Chen ◽  
Shengyi Ye ◽  
Wenyu Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Heart Disease ◽  
Deep Neural Network ◽  
Feature Selection Method ◽  
Selection Method ◽  
The Body ◽  
Prediction Algorithm ◽  
Disease Prediction ◽  
Risk Of Death

In recent decades, heart disease threatens people’s health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

2008 ◽  
Author(s):  
Xiaojia Wang ◽  
Qirong Mao ◽  
Yongzhao Zhan ◽  
Theodore E. Simos ◽  
George Psihoyios
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Analysis Algorithm ◽  
Contribution Analysis

scholarly journals Heart disease prediction using SVM based neuro-fuzzy technique in the cloud computing

2018 ◽  
Vol 7 (2.20) ◽  
pp. 153
Author(s):  
Dr M. Sadish Sendil
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Disease Prediction ◽  
Specific Level ◽  
Huge Data ◽  
Neuro Fuzzy ◽  
Heart Disease Diagnosis

Cloud computing is a technique for conveying on information development benefits in resources are recovered from the Internet through online based device and applications, as opposed to a speedy association with a server. Cloud has numerous applications in the meadows of education, social networking, and medicine. But the benefit of the cloud for medical reasons is seamless, specifically an account of the huge data generated by the healthcare industry. Heart disease diagnosis determination strategy is essential and significant issue for the patient's wellbeing. Furthermore, it will help to decrease infection to a more specific level. Computer-aided decision support method performs a vital task in medical line. Data mining gives the system and innovation to change these heaps of data into effective information for decision-making. When applying data mining techniques it carries shorter time for the prediction of the disease with more exactness. The hybrid work of preprocessing, feature selection using SVM and SVM based Neuro-Fuzzy data mining strategies utilizing as a part of the determination of the heart disease is incredibly impressive. The framework is to build up a technique for arranging for heart level of the patient relies upon highlight information utilizing Neuro-Fuzzy surmising system. The experiment is done with two different analysis that is one with preprocessed data alone and applied SVM based Neuro Fuzzy Technique and the second one is accomplished with feature selection done data and applied SVM based Neuro Fuzzy Technique. The results prove that the system result of the first one gives 92% accuracy in the heart disease prediction. The second one is giving 95.11% accuracy in the heart disease prediction.  

scholarly journals An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 271 ◽  
Author(s):  
Md Akizur Rahman ◽  
Ravie Chandren Muniyandi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Classification ◽  
Neuron Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Risk Of Cancer

An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.

A NEURAL NETWORK MODEL WITH FEATURE SELECTION FOR KOREAN SPEECH ACT CLASSIFICATION

2004 ◽  
Vol 14 (06) ◽  
pp. 407-414 ◽  
Author(s):  
KYUNGSUN KIM ◽  
HARKSOO KIM ◽  
JUNGYUN SEO
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Speech Act ◽  
Feature Selection Method ◽  
Selection Method ◽  
Morphological Features ◽  
Experimental Result ◽  
Real Field

A speech act is a linguistic action intended by a speaker. Speech act classification is an essential part of a dialogue understanding system because the speech act of an utterance is closely tied with the user's intention in the utterance. We propose a neural network model for Korean speech act classification. In addition, we propose a method that extracts morphological features from surface utterances and selects effective ones among the morphological features. Using the feature selection method, the proposed neural network can partially increase precision and decrease training time. In the experiment, the proposed neural network showed better results than other models using comparatively high-level linguistic features. Based on the experimental result, we believe that the proposed neural network model is suitable for real field applications because it is easy to expand the neural network model into other domains. Moreover, we found that neural networks can be useful in speech act classification if we can convert surface sentences into vectors with fixed dimensions by using an effective feature selection method.

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