痴呆患者的方面分析

Saloni Gupta, Riya Sharma, N. Sharma, M. Mangla
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引用次数: 0

摘要

精神认知障碍(MCD)的早期诊断对于建立及时治疗以延缓疾病的进一步发展至关重要。本文演示了从Kaggle收集的数据集的数据可视化和统计数据分析。在本文中,作者对基于轻度认知障碍(MCI)障碍的数据集进行了分析。带入工作的大多数数据都不干净,需要大量的数据清理。因此,为了预处理和清理数据,我们导入了许多python库并处理NULL值和异常值。使用统计方法处理这些NULL值和异常值,并用其平均值和中位数替换。数据可视化是通过使用图表、图形和地图等视觉元素对数据进行图形化描述。当数据处理和清理完成后,通过各种图形和图表将数据可视化,创建吸引人、信息丰富、详细的图表,以最有效、最简单的方式呈现数据。结果集中在不同性别的痴呆和非痴呆人群的可视化以及其他参数上,为读者提供了一个清晰的画面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aspect Analysis of Dementia Patients
Early diagnosis of Mental Cognitive Disorders (MCD) is crucial in establishing timely treatment to delay further progression of the disease. This paper demonstrates a data visualization and statistical data analysis of dataset collected from Kaggle. In this paper the authors have derived an analysis of a dataset based on Mild cognitive impairment (MCI) disorder. Most of the data that is brought to work is not clean and requires substantial amount of Data cleaning. Hence, for pre-processing and cleaning of data, we imported many python libraries and dealt with NULL values and outliers. These NULL values and outliers were treated using statistical methods and were replaced by their means and medians. Data visualization is the pictorial depiction of data by using visual elements like charts, graphs and maps. When data-processing and cleaning was completed, data was visualized with varied graphs and charts to create appealing, informative and detailed plots for presenting data in the most effective and easy way. The results focused on the visualization of demented and non-demented people of different genders along with other parameters to lay out a clear picture for the reader.
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