Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel

2010 ◽  
Vol 16 (2) ◽  
pp. 144-149 ◽  
Author(s):  
Jian Zhou ◽  
Xiu-zhi Shi ◽  
Lei Dong ◽  
Hai-yan Hu ◽  
Huai-yong Wang
Keyword(s):  
Discriminant Analysis ◽  
Analysis Model ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant

scholarly journals Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ming Ni ◽  
Xiaoming Zhou ◽  
Jingwei Liu ◽  
Haiyang Yu ◽  
Yuanxiang Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Discriminant Analysis ◽  
Diffusion Weighted Mri ◽  
Analysis Model ◽  
Fisher Discriminant Analysis ◽  
Ki 67 ◽  
Luminal B ◽  
Model Based ◽  
Fisher Discriminant ◽  
Diffusion Weighted

Abstract Background The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer. Methods Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups. Results A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79, 77, 88, 92 and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67. Conclusions The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

scholarly journals Prediction of the clinicopathological subtypes of breast cancer using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

2020 ◽  
Author(s):  
Ming Ni ◽  
Xiaoming Zhou ◽  
Jingwei Liu ◽  
Haiyang Yu ◽  
Yuanxiang Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Discriminant Analysis ◽  
Diffusion Weighted Mri ◽  
Analysis Model ◽  
Fisher Discriminant Analysis ◽  
Ki 67 ◽  
Luminal B ◽  
Model Based ◽  
Fisher Discriminant ◽  
Diffusion Weighted

Abstract Background: The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer.Methods: Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups.Results: A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92% to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79%, 77%, 88%, 92% and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67.Conclusions: The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

scholarly journals KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019)

2020 ◽  
Vol 8 (2) ◽  
pp. 88
Author(s):  
Amanah Saeroni ◽  
Memi Nor Hayati ◽  
Rito Goejantoro
Keyword(s):  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Customer Data ◽  
Independent Variables ◽  
Premium Payment ◽  
Multivariate Technique ◽  
Fisher Discriminant

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.

scholarly journals Prediction of the clinicopathological subtypes of breast cancer using a Fisher discriminant analysis model based on radiomic features ofdiffusion-weighted MRI

2020 ◽  
Author(s):  
Ming Ni ◽  
Xiaoming Zhou ◽  
Jingwei Liu ◽  
Haiyang Yu ◽  
Yuanxiang Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Discriminant Analysis ◽  
Diffusion Weighted Mri ◽  
Analysis Model ◽  
Fisher Discriminant Analysis ◽  
Ki 67 ◽  
Luminal B ◽  
Model Based ◽  
Fisher Discriminant ◽  
Diffusion Weighted

Abstract Background: The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer.Methods: Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups.Results: A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92% to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79%, 77%, 88%, 92% and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67.Conclusions: The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

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