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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Handling Variables, via Inversion of Partial Least Squares Models for Class-Modelling, to Bring Defective Items to

Santiago Ruiz¹, Luis Antonio Sarabia¹, María Sagrario Sánchez¹

¹Department Matemáticas y Computación, Facultad de Ciencias, Universidad de Burgos, Burgos, Spain.

Frontiers in Chemistry

|July 30, 2021

Summary

This summary is machine-generated.

This study introduces a method to define class boundaries using sensitivity and specificity in Partial Least Squares for Class-Modelling (PLS-CM). It shows how to adjust input variables to reclassify defective objects as adequate.

Keywords:

attributes authentication class-modelling latent variables model inversion partial least squares process analytical technology sensitivity/specificity

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Area of Science:

Chemometrics
Machine Learning
Data Science

Background:

Binary class-modelling is crucial for classification tasks.
Defining precise class boundaries with specific performance metrics like sensitivity and specificity is challenging.
Probabilistic models offer a flexible framework for class definition.

Purpose of the Study:

To compute linear boundaries of a class-model in input space using given sensitivity and specificity values.
To develop a method for guiding the modification of input variables to shift objects towards a desired class.
To address the practical problem of reclassifying 'defective' objects into the 'adequate' class.

Main Methods:

Inversion of a decision threshold within probabilistic class-models generated by Partial Least Squares for Class-Modelling (PLS-CM).
Characterization of boundary hyperplanes in both latent and input spaces.
Calculation of directions in the input space to guide objects towards the target class model.

Main Results:

The study successfully computes linear boundaries for class-models based on specified sensitivity and specificity.
It provides a computational procedure to determine how input variables should be modified.
Demonstrates the application in reclassifying defective objects into the adequate class, relevant for process control and product formulation.

Conclusions:

The proposed method effectively defines class boundaries and provides actionable insights for variable manipulation.
This technique offers a valuable tool for optimizing classification and improving product quality or process outcomes.
The approach is particularly useful when aiming to transform undesirable instances into desirable ones within a defined model.