ArticleComputational and mathematical methods in medicine2020
Auxiliary Medical Decision System for Prostate Cancer Based on Ensemble Method.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System.Computational intelligence and neuroscience · 2022Article
- Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation.Computational intelligence and neuroscience · 2022Article
- BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation.Computational intelligence and neuroscience · 2022Article
- A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries.Computational intelligence and neuroscience · 2022Article
- Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.Computational and mathematical methods in medicine · 2022Article
- A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- A Staging Auxiliary Diagnosis Model for Nonsmall Cell Lung Cancer Based on the Intelligent Medical System.Computational and mathematical methods in medicine · 2021Article
- A Systematic Review of Artificial Intelligence-Based Clinical Decision Support Systems in Prostate Cancer Management.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is one of the main diseases that endanger men's health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance.
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