1
|
Li KH, Chien CY, Tai SY, Chan LP, Chang NC, Wang LF, Ho KY, Lien YJ, Ho WH. Prognosis Prediction of Sudden Sensorineural Hearing Loss Using Ensemble Artificial Intelligence Learning Models. Otol Neurotol 2024; 45:759-764. [PMID: 38918073 DOI: 10.1097/mao.0000000000004241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
OBJECTIVE We used simple variables to construct prognostic prediction ensemble learning models for patients with sudden sensorineural hearing loss (SSNHL). STUDY DESIGN Retrospectively study. SETTING Tertiary medical center. PATIENTS 1,572 patients with SSNHL. INTERVENTION Prognostic. MAIN OUTCOME MEASURES We selected four variables, namely, age, days after onset of hearing loss, vertigo, and type of hearing loss. We also compared the accuracy between different ensemble learning models based on the boosting, bagging, AdaBoost, and stacking algorithms. RESULTS We enrolled 1,572 patients with SSNHL; 73.5% of them showed improving and 26.5% did not. Significant between-group differences were noted in terms of age ( p = 0.011), days after onset of hearing loss ( p < 0.001), and concurrent vertigo ( p < 0.001), indicating that the patients who showed improving to treatment were younger and had fewer days after onset and fewer vertigo symptoms. Among ensemble learning models, the AdaBoost algorithm, compared with the other algorithms, achieved higher accuracy (82.89%), higher precision (86.66%), a higher F1 score (89.20), and a larger area under the receiver operating characteristics curve (0.79), as indicated by test results of a dataset with 10 independent runs. Furthermore, Gini scores indicated that age and days after onset are two key parameters of the predictive model. CONCLUSIONS The AdaBoost model is an effective model for predicting SSNHL. The use of simple parameters can increase its practicality and applicability in remote medical care. Moreover, age may be a key factor influencing prognosis.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Yu-Jui Lien
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
| | | |
Collapse
|
2
|
Wu C, Hwang M, Huang TH, Chen YMJ, Chang YJ, Ho TH, Huang J, Hwang KS, Ho WH. Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation. BMC Bioinformatics 2021; 22:93. [PMID: 34749631 PMCID: PMC8576960 DOI: 10.1186/s12859-021-04000-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 02/05/2021] [Indexed: 12/03/2022] Open
Abstract
Background Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. Results This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. Conclusion In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model’s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.
Collapse
Affiliation(s)
- Cai Wu
- Department of Hematology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, No. 1, Shangcheng Road, Yiwu, Zhejiang, China
| | - Maxwell Hwang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou, Zhejiang, China
| | - Tian-Hsiang Huang
- Center for Big Data Research, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan
| | - Yen-Ming J Chen
- Department of Logistics Management, National Kaohsiung University of Science and Technology, No.1, University Road, Kaohsiung, 824, Taiwan
| | - Yiu-Jen Chang
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan
| | - Tsung-Han Ho
- Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan, 701, Taiwan
| | - Jian Huang
- Department of Hematology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, No. 1, Shangcheng Road, Yiwu, Zhejiang, China
| | - Kao-Shing Hwang
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan. .,Department of Electrical Engineering, National Sun Yat-Sen University, No.70, Lienhai Road, Kaohsiung, 804, Taiwan.
| | - Wen-Hsien Ho
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan. .,Department of Medical Research, Kaohsiung Medical University Hospital, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.
| |
Collapse
|
3
|
Ho WH, Huang TH, Yang PY, Chou JH, Qu JY, Chang PC, Chou FI, Tsai JT. Robust optimization of convolutional neural networks with a uniform experiment design method: a case of phonocardiogram testing in patients with heart diseases. BMC Bioinformatics 2021; 22:92. [PMID: 34749632 PMCID: PMC8576886 DOI: 10.1186/s12859-021-04032-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background Heart sound measurement is crucial for analyzing and diagnosing patients with heart diseases. This study employed phonocardiogram signals as the input signal for heart disease analysis due to the accessibility of the respective method. This study referenced preprocessing techniques proposed by other researchers for the conversion of phonocardiogram signals into characteristic images composed using frequency subband. Image recognition was then conducted through the use of convolutional neural networks (CNNs), in order to classify the predicted of phonocardiogram signals as normal or abnormal. However, CNN requires the tuning of multiple hyperparameters, which entails an optimization problem for the hyperparameters in the model. To maximize CNN robustness, the uniform experiment design method and a science-based methodical experiment design were used to optimize CNN hyperparameters in this study. Results An artificial intelligence prediction model was constructed using CNN, and the uniform experiment design method was proposed to acquire hyperparameters for optimal CNN robustness. The results indicate Filters (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{1}$$\end{document}X1), Stride (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{3}$$\end{document}X3), Activation functions (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{6}$$\end{document}X6), and Dropout (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{7}$$\end{document}X7) to be significant factors considerably influencing the ability of CNN to distinguish among heart sound states. Finally, the confirmation experiment was conducted, and the hyperparameter combination for optimal model robustness was Filters (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{1}$$\end{document}X1) = 32, Kernel Size (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{2})$$\end{document}X2) = 3 × 3, Stride (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{3}$$\end{document}X3) = (1,1), Padding (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{4})$$\end{document}X4) as same, Optimizer (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{5})$$\end{document}X5) as the stochastic gradient descent, Activation functions (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{6}$$\end{document}X6) as relu, and Dropout (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$${X}_{7}$$\end{document}X7) = 0.544. With this combination of parameters, the model had an average prediction accuracy rate of 0.787 and standard deviation of 0. Conclusion In this study, phonocardiogram signals were used for the early prediction of heart diseases. The science-based and methodical uniform experiment design was used for the optimization of CNN hyperparameters to construct a CNN with optimal robustness. The results revealed that the constructed model exhibited robustness and an acceptable accuracy rate. Other literature has failed to address hyperparameter optimization problems in CNN; a method is subsequently proposed for robust CNN optimization, thereby solving this problem.
Collapse
Affiliation(s)
- Wen-Hsien Ho
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.,Department of Medical Research, Kaohsiung Medical University Hospital, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan
| | - Tian-Hsiang Huang
- Center for Big Data Research, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan
| | - Po-Yuan Yang
- Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Road, Taichung, 407, Taiwan
| | - Jyh-Horng Chou
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.,Department of Mechanical Engineering, National Chung-Hsing University, No. 145, Xingda Road, Taichung, 402, Taiwan.,Department of Electrical Engineering, National Kaohsiung University of Science and Technology, No. 415, Chien-Kung Road, Kaohsiung, 807, Taiwan
| | - Jin-Yi Qu
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, No. 415, Chien-Kung Road, Kaohsiung, 807, Taiwan
| | - Po-Chih Chang
- Division of Thoracic Surgery, Department of Surgery, Kaohsiung Medical University Hospital, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.,Weight Management Center, Kaohsiung Medical University Hospital, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.,College of Medicine, Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.,Department of Sports Medicine, College of Medicine, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan
| | - Fu-I Chou
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, No. 415, Chien-Kung Road, Kaohsiung, 807, Taiwan.
| | - Jinn-Tsong Tsai
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan. .,Department of Computer Science, National Pingtung University, No. 4-18, Min-Sheng Road, Pingtung, 900, Taiwan.
| |
Collapse
|