Related Experiment Video
Updated: Jun 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A diagnostic methodology for hazy data with "borderline" cases
1School of Health Administration, Texas State University, San Marcos, TX 78666, USA. rs25@txstate.edu
Abstract:
Several illnesses like the hypertension, the dementia among others in reality contain borderline cases. These "borderline" (alternately mentioned hazy) cases are neither labeled healthy nor diseased by the medical professionals. Consequently, the current diagnostic test methodology is inappropriate for data with the borderline cases. A new methodology is greatly needed to analyze and interpret diagnostic test data with the borderline cases. In this article, a new methodology is therefore developed, discussed, and illustrated. The medical parameters: the sensitivity, the specificity, and the disease prevalence of the sampled participants are calculated and interpreted using the new methodology with data on borderline dementia and blood pressure cases separately.
Related Concept Videos
Detection of Gross Error: The Q Test
Quantifying and Rejecting Outliers: The Grubbs Test
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Hypothesis: Accept or Fail to Reject?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...