Development of a Deep Learning Model for the Prognosis of the Occurrence of Death from Stomach Cancer in Senegal  

作　　者：Idrissa Sy Mouhamad Mounirou Allaya Mamadou Bousso Ayoub Insa Corréa Aba Diop Youssouphe Guissé Sérigne Souaibou Diop Madieng Dieng Idrissa Sy;Mouhamad Mounirou Allaya;Mamadou Bousso;Ayoub Insa Corréa;Aba Diop;Youssouphe Guissé;Sérigne Souaibou Diop;Madieng Dieng(Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of This (UIDT), This, Senegal;Department of Computing and Management, Iba Der Thiam University of This (UIDT), This, Senegal;Department of Civil Engineering, Iba Der Thiam University of This (UIDT), This, Senegal;Department of Mathematics, Alioune Diop University of Bambey (UADB), Bambey, Senegal;Department of Surgery and Surgical Specialties, Faculty of Medicine, Pharmacy and Odonto-Stomatology, Cheikh Anta Diop University of Dakar (UCAD), Dakar, Senegal)

机构地区：[1]Department of Science, Technology, Mathematics and Engineering, Iba Der Thiam University of This (UIDT), This, Senegal [2]Department of Computing and Management, Iba Der Thiam University of This (UIDT), This, Senegal [3]Department of Civil Engineering, Iba Der Thiam University of This (UIDT), This, Senegal [4]Department of Mathematics, Alioune Diop University of Bambey (UADB), Bambey, Senegal [5]Department of Surgery and Surgical Specialties, Faculty of Medicine, Pharmacy and Odonto-Stomatology, Cheikh Anta Diop University of Dakar (UCAD), Dakar, Senegal

出　　处：《Journal of Intelligent Learning Systems and Applications》2024年第4期341-362,共22页智能学习系统与应用（英文）

摘　　要：Context and Objectives: Stomach cancer ranks fifth in incidence and fourth in mortality worldwide. In Senegal, there were 597 new cases in 2020, with a mortality rate of almost 70%. The aim of this study was to develop a machine-learning model for the prognosis of death from stomach cancer 5 years after treatment. Methods: Our study sample consisted of 262 patients treated for gastric cancer at Aristide le Dantec Hospital and followed postoperatively between 2007 and 2020. We developed a multilayer perceptron with optimal hyperparameters and compared its performance with standard classification algorithms. We also augmented our data with a set of synthetic data generators to evaluate the behaviour of the model when faced with a larger amount of data. Results: Our model obtained an accuracy of 97.5%, outperforming the SVM (93%), RF (93.8%) and KNN (92.7%) models. An improvement of 1.5% in accuracy was achieved with synthetic data. Our study showed that the most pejorative factors in the evolution of the cancer were the appearance of hepatic metastases or adenopathy, smoking, and the infiltrative and stenosing aspects of the tumour on endoscopy. Conclusion: Our model predicted the occurrence of death from gastric cancer with very high accuracy, outperforming standard classification algorithms. The increase in training data produced an improvement in accuracy. Our study will help doctors to personalize the management of gastric cancer patients.Context and Objectives: Stomach cancer ranks fifth in incidence and fourth in mortality worldwide. In Senegal, there were 597 new cases in 2020, with a mortality rate of almost 70%. The aim of this study was to develop a machine-learning model for the prognosis of death from stomach cancer 5 years after treatment. Methods: Our study sample consisted of 262 patients treated for gastric cancer at Aristide le Dantec Hospital and followed postoperatively between 2007 and 2020. We developed a multilayer perceptron with optimal hyperparameters and compared its performance with standard classification algorithms. We also augmented our data with a set of synthetic data generators to evaluate the behaviour of the model when faced with a larger amount of data. Results: Our model obtained an accuracy of 97.5%, outperforming the SVM (93%), RF (93.8%) and KNN (92.7%) models. An improvement of 1.5% in accuracy was achieved with synthetic data. Our study showed that the most pejorative factors in the evolution of the cancer were the appearance of hepatic metastases or adenopathy, smoking, and the infiltrative and stenosing aspects of the tumour on endoscopy. Conclusion: Our model predicted the occurrence of death from gastric cancer with very high accuracy, outperforming standard classification algorithms. The increase in training data produced an improvement in accuracy. Our study will help doctors to personalize the management of gastric cancer patients.

关 键 词：Artificial Intelligence Multilayer Perceptron PROGNOSIS Gastric Cancer Synthetic Data 

分 类 号：R73[医药卫生—肿瘤]