Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may preven...Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。展开更多
During the Fifth International Conference onEnvironmental Mutagens which was held in Cleveland,Ohio,USA on July 10-15,1989,the idea of organizing anInternational Symposium on Environmental Mutagenesisand Carcinogenesi...During the Fifth International Conference onEnvironmental Mutagens which was held in Cleveland,Ohio,USA on July 10-15,1989,the idea of organizing anInternational Symposium on Environmental Mutagenesisand Carcinogenesis in China was conceived.The ChineseEnvironmental Mutagen Society and the Institute of Genet-ics of Fudan University accepted the responsibility.展开更多
This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. ...This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.展开更多
Many reviewers have contributed their expertise and time to the peer review,a critical process to ensure the quality of World Journal of Gastrointestinal
Foundry Management＆Technology（美国,英文）地址：The Penton Media Building 1300 E.9th Street Cleveland,OH 44114-1503Tel：＋1-216-696-7000Fax：＋1-216-696-7932E-mail：robert.brooks@penton.com http：//www.foundrymag.com...Foundry Management＆Technology（美国,英文）地址：The Penton Media Building 1300 E.9th Street Cleveland,OH 44114-1503Tel：＋1-216-696-7000Fax：＋1-216-696-7932E-mail：robert.brooks@penton.com http：//www.foundrymag.com1703001展望无机粘结剂——越来越多的铸造厂正在从有机粘结剂型砂系统转变为无机粘结剂型砂系统.2016,144（7）：封二页1703002对所有铸造工作者的问候——编者寄语.展开更多
Many reviewers have contributed their expertise and time to the peer review, a critical process to ensure the quality of World Journal of Gastrointestinal Pharmacology and Therapeutics.
文摘Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。
文摘During the Fifth International Conference onEnvironmental Mutagens which was held in Cleveland,Ohio,USA on July 10-15,1989,the idea of organizing anInternational Symposium on Environmental Mutagenesisand Carcinogenesis in China was conceived.The ChineseEnvironmental Mutagen Society and the Institute of Genet-ics of Fudan University accepted the responsibility.
文摘This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.
文摘Many reviewers have contributed their expertise and time to the peer review,a critical process to ensure the quality of World Journal of Gastrointestinal
文摘Foundry Management＆Technology（美国,英文）地址：The Penton Media Building 1300 E.9th Street Cleveland,OH 44114-1503Tel：＋1-216-696-7000Fax：＋1-216-696-7932E-mail：robert.brooks@penton.com http：//www.foundrymag.com1703001展望无机粘结剂——越来越多的铸造厂正在从有机粘结剂型砂系统转变为无机粘结剂型砂系统.2016,144（7）：封二页1703002对所有铸造工作者的问候——编者寄语.
文摘Many reviewers have contributed their expertise and time to the peer review, a critical process to ensure the quality of World Journal of Gastrointestinal Pharmacology and Therapeutics.
