Comparision of Four Machine Learning Techniques for the Prediction of Prostate Cancer Survivability

Wen, Huaiyu; Li, Sufang; Li, Wei; Li, Minglu; Yin, Chang

doi:10.1109/iccwamtip.2018.8632577

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…Unfortunately, they considered a limited dataset without imputing the censored cases. Other sophisticated models based on active learning have been used to improve Cox regression and to predict prostate cancer survival among patients in the Surveillance, Epidemiology, and End Results (SEER) database, with c-indexes over 0.8 ( 22 , 23 ).…”

Section: Discussionmentioning

confidence: 99%

Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy

et al. 2021

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BackgroundUse of predictive models for the prediction of biochemical recurrence (BCR) is gaining attention for prostate cancer (PCa). Specifically, BCR occurs in approximately 20–40% of patients five years after radical prostatectomy (RP) and the ability to predict BCR may help clinicians to make better treatment decisions. We aim to investigate the accuracy of CAPRA score compared to others models in predicting the 3-year BCR of PCa patients.Material and MethodsA total of 5043 men who underwent RP were analyzed retrospectively. The accuracy of CAPRA score, Cox regression analysis, logistic regression, K-nearest neighbor (KNN), random forest (RF) and a densely connected feed-forward neural network (DNN) classifier were compared in terms of 3-year BCR predictive value. The area under the receiver operating characteristic curve was mainly used to assess the performance of the predictive models in predicting the 3 years BCR of PCa patients. Pre-operative data such as PSA level, Gleason grade, and T stage were included in the multivariate analysis. To measure potential improvements to the model performance due to additional data, each model was trained once more with an additional set of post-operative surgical data from definitive pathology.ResultsUsing the CAPRA score variables, DNN predictive model showed the highest AUC value of 0.7 comparing to the CAPRA score, logistic regression, KNN, RF, and cox regression with 0.63, 0.63, 0.55, 0.64, and 0.64, respectively. After including the post-operative variables to the model, the AUC values based on KNN, RF, and cox regression and DNN were improved to 0.77, 0.74, 0.75, and 0.84, respectively.ConclusionsOur results showed that the DNN has the potential to predict the 3-year BCR and outperformed the CAPRA score and other predictive models.

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Section: Discussionmentioning

confidence: 99%

Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy

et al. 2021

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“…Ensemble learning methods that train a number of weak base learners and then combine their outputs are popular in medical prediction researches [22]. Many researchers conducted their researches on cases collected from SEER database [5][6][14][15][16][17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Liu¹,

Li²,

Chen³

et al. 2020

7th International Conference on Artificial Intelligence and Applications

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Cancer is one of the most common causes of death in the world, while gastric cancer has the highest incidence in Asia. Predicting gastric cancer patients' survivability can inform patients care decisions and help doctors prescribe personalized medicine. Classification techniques have been widely used to predict survivability of cancer patients. However, very few attention has been paid to patients who cannot survive. In this research, we consider survival prediction to be a twostaged problem. The first is to predict the patients' five-year survivability. If the patient's predicted outcome is death, the second stage predicts the remaining lifespan of the patient. Our research proposes a custom ensemble method which integrated multiple machine learning algorithms. It exhibits a significant predictive improvement in both stages of prediction, compared with the state-of-the-art machine learning techniques. The base machine learning techniques include Decision Trees, Random Forest, Adaboost, Gradient Boost Machine (GBM), Artificial Neural Network (ANN), and the most popular GBM framework-LightGBM. The model is comprehensively evaluated on open source cancer data provided by the Surveillance, Epidemiology, and End Results Program (SEER) in terms of accuracy, area under the curve, Fscore, precision, recall rate, training and predicting time in the classification stage, and root mean squared error, mean absolute error, coefficient of determination (R 2) in the regression stage.

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“…Firstly, a general overview of transfer learning is presented. Traditional machine learning has achieved significant performance [6], [7]. A common assumption in traditional machine learning is that training samples and test samples are in the same feature space and follow the same data distribution.…”

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confidence: 99%

Semisupervised Dual-Dictionary Learning for Heterogeneous Transfer Learning on Cross-Scene Hyperspectral Images

Chen¹,

Ye²,

Ling³

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Lack of labeled training samples is a big challenge for hyperspectral image (HSI) classification. In recent years, cross-scene classification has become a new research topic. In cross-scene classification, two closely related HSI scenes are considered, one contains adequate labeled samples, namely source scene, while the other one contains only a few labeled samples, namely target scene. The goal of cross-scene classification is utilizing the labeled samples in source scene to benefit the classification in target scene. In most cases, different HSIs are imaged by different sensors, leading to different feature dimensions (numbers of bands) in different scenes. In this situation, heterogeneous transfer learning is demanded. In this work, we propose a heterogeneous transfer learning algorithm namely semisupervised dual-dictionary nonnegative matrix factorization (SS-DDNMF). SS-DDNMF consists of two contributions. 1) Dualdictionary nonnegative matrix factorization (DDNMF): DDNMF trains two dictionaries for source and target scenes, respectively, aiming at projecting the source and target features to a shared low-dimensional subspace, eliminating the difference between feature spaces. In DDNMF, within-scene and cross-scene graphs are built to maintain the similarities between pixels. 2) Semisupervised learning for target scene: as the limited number of labeled pixels in target scene will affect the graph building of DDNMF, semisupervised learning is adopted in target scene. In details, superpixel segmentation is adopted to generate pseudo labels for some unlabeled pixels, thus more "labeled" pixels can be considered for building better graphs. The effectiveness of SS-DDNMF is verified by experiments on cross-scene HSIs.

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Comparision of Four Machine Learning Techniques for the Prediction of Prostate Cancer Survivability

Cited by 17 publications

References 4 publications

Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy

Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy

Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Semisupervised Dual-Dictionary Learning for Heterogeneous Transfer Learning on Cross-Scene Hyperspectral Images

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