Radiomic Features Based Severity Prediction in Dementia MR Images Using Hybrid SSA-PSO Optimizer and Multi-class SVM Classifier

Priyanka, Ahana; Ganesan, Kavitha

doi:10.1016/j.irbm.2022.05.003

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…SVM, RF, and Cox regression with nomogram showed better performance than LR and CNN in the prediction of CRCLM. SVM can effectively handle complex and high-dimensional clinical and radiomic features, capturing intricate patterns and non-linear relationships, which is crucial for accurate predictions [ 62 ]. The ability of SVM to separate data points into different classes by finding an optimal hyperplane maximally distant from the data points of different classes enhances its predictive accuracy [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

Seow-En,

Koh,

Zhao

et al. 2023

Ann Hepatobiliary Pancreat Surg

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This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

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Section: Discussionmentioning

confidence: 99%

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

Seow-En,

Koh,

Zhao

et al. 2023

Ann Hepatobiliary Pancreat Surg

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Video Refereeing Model of Soccer Match Based on Fuzzy Clustering and Cuckoo Optimization Algorithm

Wang,

Geng,

Wang

et al. 2024

IEEE Access

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Dysarthria Speech Detection Using Convolutional Neural Networks with Gated Recurrent Unit

Shih

Liao

et al. 2022

Healthcare

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In recent years, due to the rise in the population and aging, the prevalence of neurological diseases is also increasing year by year. Among these patients with Parkinson’s disease, stroke, cerebral palsy, and other neurological symptoms, dysarthria often appears. If these dysarthria patients are not quickly detected and treated, it is easy to cause difficulties in disease course management. When the symptoms worsen, they can also affect the patient’s psychology and physiology. Most of the past studies on dysarthria detection used machine learning or deep learning models as classification models. This study proposes an integrated CNN-GRU model with convolutional neural networks and gated recurrent units to detect dysarthria. The experimental results show that the CNN-GRU model proposed in this study has the highest accuracy of 98.38%, which is superior to other research models.

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Radiomic Features Based Severity Prediction in Dementia MR Images Using Hybrid SSA-PSO Optimizer and Multi-class SVM Classifier

Cited by 4 publications

References 28 publications

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

Video Refereeing Model of Soccer Match Based on Fuzzy Clustering and Cuckoo Optimization Algorithm

Dysarthria Speech Detection Using Convolutional Neural Networks with Gated Recurrent Unit

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