Department of Medical Laboratory Techniques, Al-Maarif University College, Al-Anbar, Iraq.
College of Technical Engineering, Al-Farahidi University, Baghdad, Iraq.
Comput Intell Neurosci. 2022 May 31;2022:6058213. doi: 10.1155/2022/6058213. eCollection 2022.
This paper presents the research results on the contribution of user-centered data mining based on the standard principles, focusing on the analysis of survival and mortality of lung cancer cases. Researchers used anonymized data from previously diagnosed instances in the health database to predict the condition of new patients who have not had their results yet. Medical professionals specializing in this field provided feedback on the usefulness of the new software, which was constructed using WEKA data mining tools and the Naive Bayes method. The results of this article provide elements of interest to discuss the value of identifying or discovering relationships in apparently "hidden" information to propose strategies to counteract health problems or prevent future complications and thus contribute to improving the quality of care. Life of the population, as would be the case of data mining in the health area, has shown applicability in the early detection and prevention of diseases for the analysis of genetic markers to determine the probability of a satisfactory response to medical treatment, and the most accurate model was Naive Bayes (91.1%). The Naive Bayes algorithm's closest competitor, bagging, came in second with 90.8%. The analysis found that the ZeroR algorithm had the lowest success rate at 80%.
本文介绍了基于标准原则的以用户为中心的数据挖掘研究成果，重点分析了肺癌病例的生存和死亡情况。研究人员使用健康数据库中先前诊断病例的匿名数据来预测尚未得出结果的新患者的病情。专门从事该领域的医学专业人员对新软件的有用性提供了反馈，该软件是使用 WEKA 数据挖掘工具和朴素贝叶斯方法构建的。本文的研究结果提供了有价值的讨论素材，探讨了识别或发现看似“隐藏”信息中的关系的价值，提出了应对健康问题或预防未来并发症的策略，从而有助于提高人口的护理质量。在医疗领域进行数据挖掘，已显示出在早期检测和预防疾病方面的适用性，例如分析遗传标记以确定对治疗的满意反应的可能性，而最准确的模型是朴素贝叶斯（91.1%）。朴素贝叶斯算法的最接近竞争对手——袋装法，以 90.8%的准确率位居第二。分析发现，零算法的成功率最低，为 80%。
