Efficient Missing Data Technique for Prediction of Nasopharyngeal Carcinoma Recurrence

Ritthiprav, Panrasee; Kumdee, Orrawan; Bhongmakap, Thongchai

doi:10.3923/itj.2013.1125.1133

Cited by 2 publications

(10 citation statements)

References 10 publications

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“…It was observed that the majority of the papers applying DL models used various forms of CNN (n=30), 15 , 18 , 19 , 21–24 , 28–34 , 36 , 45–53 , 55 , 56 , 60 , 65 , 67 , 69 while the main ML method used was ANN (n=12). 13 , 16 , 26 , 42–44 , 54 , 61–64 , 68…”

Section: Resultsmentioning

confidence: 99%

“…ANN is the backbone of DL, as DL algorithms are ANNs with multiple (2 or more) hidden layers. In the development of AI applications for NPC, 80% of the studied articles incorporated either ANN or DL technique in their models 12 , 13 , 15–19 , 21–26 , 28–34 , 36 , 38 , 39 , 42–56 , 60–69 because neural networks are generally better for image recognition. However, one study cautioned that ANNs were not necessarily better than other ML models in NPC identification.…”

Section: Discussionmentioning

confidence: 99%

“…Classification performance - AUC: 0.8809 - Sensitivity: 0.8003 - Specificity: 0.8486 - Accuracy: 0.8245 2. Predictive performance - Mean square error: 0.595360 G-NN-SIRM had a significantly higher performance than the other techniques in NPC recurrence prediction The G-NN-SIRM can be applied to NPC recurrence prediction N/A Ritthipravat, Kumdee, and Bhongmakapat (2013) 13 (Thailand) NPC 495 (Clinicopathological and serology data) Machine learning (Prognosis) 1. Missing data technique to complete data for model training: Complete-case analysis, Mean imputation, expectation-maximization imputation and KNN imputation 2.…”

Section: Methodsmentioning

confidence: 99%

“… ✓ ✓ ✓ ✓ ✓ ✓ Yes No Aussem et al (2012) 11 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Kumdee, Bhongmakapat and Ritthipravat (2012) 12 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Ritthipravat, Kumdee, and Bhongmakapat (2013) 13 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Zhu and Kan (2014) 44 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Jiang et al (2016) 14 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Liu et al (2016) 43 ✓ ✓ ✓ ?…”

Section: Methodsmentioning

confidence: 99%

“…29,33,35 Furthermore, 4 studies used treatment plans, [64][65][66]68 while proteins and microRNA expressions data were each extracted by one study. 10,44 There were also four articles that trained with both clinicopathological and plasma EBV DNA/serology data, [12][13][14]17 while one article trained its model with clinicopathological and dosimetric data. 57 Risk factors (n=2), such as demographic, medical history, familial cancer history, dietary, social and environmental factors, were also used to develop AI models.…”

Section: Characteristics Of Relevant Studiesmentioning

confidence: 99%

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Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Ng¹,

But²,

Choi

et al. 2022

CMAR

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Introduction Nasopharyngeal carcinoma (NPC) is endemic to Eastern and South-Eastern Asia, and, in 2020, 77% of global cases were diagnosed in these regions. Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. Methods The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles. Results A total of 78 articles were reviewed after removing duplicates and papers that did not meet the inclusion and exclusion criteria. After quality assessment, 60 papers were included in the current study. There were four main types of applications, which were auto-contouring, diagnosis, prognosis, and miscellaneous applications (especially on radiotherapy planning). The different forms of convolutional neural networks (CNNs) accounted for the majority of DL algorithms used, while the artificial neural network (ANN) was the most frequent ML model implemented. Conclusion There is an overall positive impact identified from AI implementation in the management of NPC. With improving AI algorithms, we envisage AI will be available as a routine application in a clinical setting soon.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Characteristics Of Relevant Studiesmentioning

confidence: 99%

See 3 more Smart Citations

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Ng¹,

But²,

Choi

et al. 2022

CMAR

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Automatic classification of respiratory patterns involving missing data imputation techniques

Hernández-Pereira

Álvarez-Estévez

Moret‐Bonillo

2015

Biosystems Engineering

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A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that in general, the Self-Organising Map imputation method allows non-tree based classifiers to achieve improvements over the rest of the imputation methods in terms of the classification accuracy, and that the Feedforward neural network and the Random Forest classifiers offer the best performance regardless of the imputation method used. The improvements in terms of accuracy over the previous work of the authors are limited but the Feed Forward neural network model achieves promising results.

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Efficient Missing Data Technique for Prediction of Nasopharyngeal Carcinoma Recurrence

Cited by 2 publications

References 10 publications

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Automatic classification of respiratory patterns involving missing data imputation techniques

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