An FCR Approach Towards Detection of Outliers for Medical Data

Sidra Iqbal, Hafiz Bahloul Ajmeri, Sumaira Bibi, Abdul Wahid
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Abstract

Regression analysis has been brought into practical functionality in this paper to intensify the magnitude of adequacy for outliers' detection in medical data. Previously, linear regression model for the outlier detection in medical data has been used. The use of linear model to detect outliers leaves a certain amount of gap in increasing the efficiency of the expected values. Linear regression basically represents linear relationship between data and sometimes loosely fits the data. The proposed FCR technique on contrary contributes towards increasing the efficiency of expected data and improved effectiveness towards detecting outliers. The proposed method is adept to best fit curve to the data. It further verifies to be a constructive asset that increases the model adequacy on medical data. The proposed approach in this paper has demonstrated to deliver enhanced adequacy paralleled to linear regression.
医疗数据异常值检测的FCR方法
本文将回归分析引入实际功能,以增强医学数据中异常值检测的充分性。以往,线性回归模型用于医学数据的异常值检测。使用线性模型检测异常值在提高期望值效率方面存在一定的差距。线性回归基本上表示数据之间的线性关系,有时松散拟合数据。相反,所提出的FCR技术有助于提高预期数据的效率和提高检测异常值的有效性。该方法能较好地拟合曲线与数据。它进一步证实是一种建设性的资产,可以提高医疗数据的模型充分性。本文提出的方法已被证明可以提供与线性回归平行的增强充分性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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