HCV患者大数据分析

Sara Khaleel, Ahed J. Alkhatib
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引用次数: 0

摘要

本研究旨在分析Kaggle上发布的关于HCV感染的大数据,寻找与HCV感染相关的人口学变量和临床变量之间的相关性。Kaggle上公布的数据是1385名患者的大数据。数据包括一些变量,如年龄、性别和身体质量指数(BMI)。临床表现包括发热、黄疸、头痛、恶心、呕吐等。包括实验室结果在内的变量包括白细胞、红细胞、血小板和血红蛋白。包括各种统计模型,如描述性统计,如频率、百分比、平均值和标准差。研究变量之间的相关性采用Pearson相关。α # 0.05为显著性。研究结果显示,约50%的患者报告了临床表现。结果报告了研究变量之间的一些相关性,包括HB与BMI、恶心和呕吐之间的显著正相关。黄疸与BMI呈负相关,腹泻与血红蛋白呈负相关。综上所述,我们建议未来的研究来调查这种相关性的重要性。
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Analysis of Big Data of HCV Patients
: The present study aimed to analyze big data posted on Kaggle about HCV infection and to find correlations between demographic variables and clinical variables related to HCV infection. The data posted on Kaggle is a large data consisting of 1385 patients. Data included some variables such as age, gender and Body Mass Index (BMI). Clinical manifestations were also included such as fever, jaundice, headache, nausea and vomiting. Variables including laboratory findings including white blood cells, red blood cells, platelets and hemoglobin were also included. Various statistical models were included such as descriptive statistics such as frequencies, percentages, means, and standard deviations. The correlations between study variable were assed using Pearson correlation. Significance was considered at α # 0.05. Study findings showed that clinical manifestations were reported by about 50% of patients. The results reported some correlations between study variables including positively significant correlations between HB and BMI, nausea and vomiting. Also, there were some negatively significant correlation between jaundice and BMI and diarrhea and hemoglobin. Taken together, we recommend future studies to investigate the importance of such correlations.
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