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.

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.

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

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction

2020 ◽  
Author(s):  
Yu Zhang ◽  
Cangzhi Jia ◽  
Melissa Jane Fullwood ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Neural Network ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Open Reading Frames ◽  
Nucleotide Bias ◽  
Sequencing Technologies ◽  
Type Data ◽  
Coding Potential

Abstract The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods perform well on distinguishing majority long noncoding RNAs (lncRNAs) and coding RNAs (mRNAs) but poorly on RNAs with small open reading frames (sORFs). Here, we present DeepCPP (deep neural network for coding potential prediction), a deep learning method for RNA coding potential prediction. Extensive evaluations on four previous datasets and six new datasets constructed in different species show that DeepCPP outperforms other state-of-the-art methods, especially on sORF type data, which overcomes the bottleneck of sORF mRNA identification by improving more than 4.31, 37.24 and 5.89% on its accuracy for newly discovered human, vertebrate and insect data, respectively. Additionally, we also revealed that discontinuous k-mer, and our newly proposed nucleotide bias and minimal distribution similarity feature selection method play crucial roles in this classification problem. Taken together, DeepCPP is an effective method for RNA coding potential 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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