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Ying J, Jing X, Gao F, Cheng J, Fu L, Yang H. Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1579-1590. [PMID: 38441701 PMCID: PMC11300765 DOI: 10.1007/s10278-024-01063-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 08/07/2024]
Abstract
This study aimed to develop a model based on radiomics and deep learning features to predict the ablation rate in patients with adenomyosis undergoing high-intensity focused ultrasound (HIFU) therapy. A total of 119 patients with adenomyosis who received HIFU therapy were retrospectively analyzed. Participants were included in the training and testing queues in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI) images, and VGG-19 was used to extract advanced deep features. An ensemble model based on multi-model fusion for predicting the efficacy of HIFU in adenomyosis was proposed, which consists of four base classifiers and was evaluated using accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve (AUC). The predictive performance of the combined model combining radiomics and deep learning features outperformed the radiomics and deep learning feature models alone, with accuracy of 0.848 and 0.814 in training and test sets, and AUC of 0.916 and 0.861, respectively. Compared with the base classifiers that make up the multi-model fusion model, the fusion model also exhibited better prediction performance. The fusion model incorporating both radiomics and deep learning features had certain predictive value for the ablation rate of adenomyosis under HIFU therapy and could help select patients with adenomyosis who would benefit from HIFU therapy.
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Affiliation(s)
- Jie Ying
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200000, China.
| | - Xin Jing
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200000, China
| | - Feng Gao
- Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
| | - Jiejun Cheng
- Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Le Fu
- Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
| | - Haima Yang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200000, China
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Bizimana PC, Zhang Z, Hounye AH, Asim M, Hammad M, El-Latif AAA. Automated heart disease prediction using improved explainable learning-based technique. Neural Comput Appl 2024. [DOI: 10.1007/s00521-024-09967-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/03/2024] [Indexed: 07/23/2024]
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Kapila R, Saleti S. Optimizing fetal health prediction: Ensemble modeling with fusion of feature selection and extraction techniques for cardiotocography data. Comput Biol Chem 2023; 107:107973. [PMID: 37926049 DOI: 10.1016/j.compbiolchem.2023.107973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 09/12/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Cardiotocography (CTG) captured the fetal heart rate and the timing of uterine contractions. Throughout pregnancy, CTG intelligent categorization is crucial for monitoring fetal health and preserving proper fetal growth and development. Since CTG provides information on the fetal heartbeat and uterus contractions, which helps determine if the fetus is pathologic or not, obstetricians frequently use it to evaluate a child's physical health during pregnancy. In the past, obstetricians have artificially analyzed CTG data, which is time-consuming and inaccurate. So, developing a fetal health categorization model is crucial as it may help to speed up the diagnosis and treatment and conserve medical resources. The CTG dataset is used in this study. To diagnose the illness, 7 machine learning models are employed, as well as ensemble strategies including voting and stacking classifiers. In order to choose and extract the most significant and critical attributes from the dataset, Feature Selection (FS) techniques like ANOVA and Chi-square, as well as Feature Extraction (FE) strategies like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), are being used. We used the Synthetic Minority Oversampling Technique (SMOTE) approach to balance the dataset because it is unbalanced. In order to forecast the illness, the top 5 models are selected, and these 5 models are used in ensemble methods such as voting and stacking classifiers. The utilization of Stacking Classifiers (SC), which involve Adaboost and Random Forest (RF) as meta-classifiers for disease detection. The performance of the proposed SC with meta-classifier as RF model, which incorporates Chi-square with PCA, outperformed all other state-of-the-art models, achieving scores of 98.79%,98.88%,98.69%,96.32%, and 98.77% for accuracy, precision, recall, specificity, and f1-score respectively.
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Affiliation(s)
- Ramdas Kapila
- Data Science Laboratory, Computer Science and Engineering, SRM University - AP, India.
| | - Sumalatha Saleti
- Data Science Laboratory, Computer Science and Engineering, SRM University - AP, India.
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Bizimana PC, Zhang Z, Asim M, El-Latif AAA, Hammad M. Learning-based techniques for heart disease prediction: a survey of models and performance metrics. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 83:39867-39921. [DOI: 10.1007/s11042-023-17051-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 07/23/2024]
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Liao B, Liang J, Guo B, Jia X, Lu J, Zhang T, Sun R. ILSHIP: An interpretable and predictive model for hypothyroidism. Comput Biol Med 2023; 154:106578. [PMID: 36738707 DOI: 10.1016/j.compbiomed.2023.106578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 01/08/2023] [Accepted: 01/22/2023] [Indexed: 02/01/2023]
Abstract
Hypothyroidism is one of the common endocrine diseases, and its incidence is increasing year by year. Due to the insidious nature of this disease, it often leads to delayed treatment and even misdiagnosis. This paper proposes ILSHIP, an interpretable predictive model for hypothyroidism, to reduce its diagnostic complexity as well as improve the predictive performance and interpretability of existing models. First, the ILSHIP prediction model was built based on label encoding, missing value processing, feature selection, and data enhancement of the dataset. Second, the comprehensive performance of ILSHIP was compared with twelve existing related study models and eleven mainstream models, such as XGBoost and MLP. The experimental results showed that, based on the optimal hyperparameters the ILSHIP model can achieve 99.392%, 99.437%, 99.348%, 99.381%, and 99.960% in accuracy, recall, specificity, F1, and AUC, respectively. The accuracy of the ILSHIP model was about 0.7%-15.4% higher than the existing models. By introducing the SHAP framework into the ILSHIP model, important features affecting hypothyroidism such as thyroid stimulating hormone (TSH) and free thyroxine index (FTI) were also identified, and the influencing factors for different individuals were finally analyzed to provide a basis for medical personnel to monitor the condition.
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Affiliation(s)
- Bin Liao
- College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, PR China
| | - Jinming Liang
- College of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, 830012, PR China.
| | - Binglei Guo
- School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, 441053, PR China
| | - Xiaoyao Jia
- College of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, 830012, PR China
| | - Jiarong Lu
- College of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, 830012, PR China
| | - Tao Zhang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, PR China
| | - Ruina Sun
- College of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, 830012, PR China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, PR China; School of Networks Security, University of Chinese Academy of Sciences, Beijing, 100049, PR China
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Kibria HB, Nahiduzzaman M, Goni MOF, Ahsan M, Haider J. An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI. SENSORS (BASEL, SWITZERLAND) 2022; 22:7268. [PMID: 36236367 PMCID: PMC9571784 DOI: 10.3390/s22197268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/20/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.
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Affiliation(s)
- Hafsa Binte Kibria
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md. Omaer Faruq Goni
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
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