Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis
Aim. To develop and verify a method for diagnosis of peptic ulcer based on neural network analysis of data on patients’ risk factors.Materials and methods. This article presents the results of a study based on materials on risk factors of 488 patients. The data was analyzed using internally develope...
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Siberian State Medical University (Tomsk)
2018-09-01
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| Series: | Бюллетень сибирской медицины |
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| Online Access: | https://bulletin.ssmu.ru/jour/article/view/1287 |
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| author | V. A. Lazarenko A. E. Antonov V. K. Markapuram K. Awad |
| author_facet | V. A. Lazarenko A. E. Antonov V. K. Markapuram K. Awad |
| author_sort | V. A. Lazarenko |
| collection | DOAJ |
| description | Aim. To develop and verify a method for diagnosis of peptic ulcer based on neural network analysis of data on patients’ risk factors.Materials and methods. This article presents the results of a study based on materials on risk factors of 488 patients. The data was analyzed using internally developed artificial neural network (Certificate of State Registration of Program for Computers (RU) no. 2017613090).The results of the study. The proposed approach demonstrated the levels of sensitivity of 74.4%, m = 4.3 and specificity of 93.3%, m = 2.46 during clinical testing.The prediction of the age of probable hospitalization ensured the generation of an array of data for which the Mean Absolute Error (MAE) of the prognosis was 1.8 years, m = 0.11 in the training set and 1.9 years, m = 0.15 in the clinical testing set. The maximum of absolute prognosis error in the clinical testing set did not exceed 2.2 at p = 0.95 and 2.3 years at p = 0.99.Conclusion. A new method is proposed for diagnosis of peptic ulcer based on a neural network analysis of data on patients’ risk factors. During clinical testing of the model, this approach demonstrated Area Under the Curve (AUC) levels reaching 0.943. The use of the artificial neural network also made it possible to predict the age of probable hospitalization. The use of the neural network demonstrated additional advantages including: non-invasiveness, the lack of need to prepare the patient for the study and the possibility to obtain results immediately after the onset of the disease without a time delay for sample processing. |
| format | Article |
| id | doaj-art-da9a1105849d4528ad97f4437d3abaf7 |
| institution | Kabale University |
| issn | 1682-0363 1819-3684 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | Siberian State Medical University (Tomsk) |
| record_format | Article |
| series | Бюллетень сибирской медицины |
| spelling | doaj-art-da9a1105849d4528ad97f4437d3abaf72025-08-20T03:37:47ZengSiberian State Medical University (Tomsk)Бюллетень сибирской медицины1682-03631819-36842018-09-01173889510.20538/1682-0363-2018-3-88-95811Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysisV. A. Lazarenko0A. E. Antonov1V. K. Markapuram2K. Awad3Kursk State Medical University (KSMU)Kursk State Medical University (KSMU)Centre for Development of Advanced ComputingNational Research Center; Ruprecht-Karls University of HeidelbergAim. To develop and verify a method for diagnosis of peptic ulcer based on neural network analysis of data on patients’ risk factors.Materials and methods. This article presents the results of a study based on materials on risk factors of 488 patients. The data was analyzed using internally developed artificial neural network (Certificate of State Registration of Program for Computers (RU) no. 2017613090).The results of the study. The proposed approach demonstrated the levels of sensitivity of 74.4%, m = 4.3 and specificity of 93.3%, m = 2.46 during clinical testing.The prediction of the age of probable hospitalization ensured the generation of an array of data for which the Mean Absolute Error (MAE) of the prognosis was 1.8 years, m = 0.11 in the training set and 1.9 years, m = 0.15 in the clinical testing set. The maximum of absolute prognosis error in the clinical testing set did not exceed 2.2 at p = 0.95 and 2.3 years at p = 0.99.Conclusion. A new method is proposed for diagnosis of peptic ulcer based on a neural network analysis of data on patients’ risk factors. During clinical testing of the model, this approach demonstrated Area Under the Curve (AUC) levels reaching 0.943. The use of the artificial neural network also made it possible to predict the age of probable hospitalization. The use of the neural network demonstrated additional advantages including: non-invasiveness, the lack of need to prepare the patient for the study and the possibility to obtain results immediately after the onset of the disease without a time delay for sample processing.https://bulletin.ssmu.ru/jour/article/view/1287artificial neural networkneuronetmultilayer perceptrondiagnosisdiagnosticspeptic ulcer diseaseartificial intelligenceprognosis |
| spellingShingle | V. A. Lazarenko A. E. Antonov V. K. Markapuram K. Awad Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis Бюллетень сибирской медицины artificial neural network neuronet multilayer perceptron diagnosis diagnostics peptic ulcer disease artificial intelligence prognosis |
| title | Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis |
| title_full | Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis |
| title_fullStr | Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis |
| title_full_unstemmed | Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis |
| title_short | Experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors’ analysis |
| title_sort | experience of neuronet diagnosis and prediction of peptic ulcer disease by results of risk factors analysis |
| topic | artificial neural network neuronet multilayer perceptron diagnosis diagnostics peptic ulcer disease artificial intelligence prognosis |
| url | https://bulletin.ssmu.ru/jour/article/view/1287 |
| work_keys_str_mv | AT valazarenko experienceofneuronetdiagnosisandpredictionofpepticulcerdiseasebyresultsofriskfactorsanalysis AT aeantonov experienceofneuronetdiagnosisandpredictionofpepticulcerdiseasebyresultsofriskfactorsanalysis AT vkmarkapuram experienceofneuronetdiagnosisandpredictionofpepticulcerdiseasebyresultsofriskfactorsanalysis AT kawad experienceofneuronetdiagnosisandpredictionofpepticulcerdiseasebyresultsofriskfactorsanalysis |