Predicting Omicron - Positive or Negative?

The theory of Discriminant Analysis

Linear discriminant analysis (LDA) and the related Fisher’s linear discriminant are methods used in statistics, machine learning, and medical theories to find a linear combination of characteristics which features or divides two or more classes of objects or events. The resulting blend may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Fisher’s linear discriminant

The terms Fisher’s linear discriminant and LDA are often used interchangeably, although Fisher’s original article actually describes a slightly different discriminant, which does not make some of the assumptions of LDA such as normally distributed classes or equal class covariances.

Possible applications:

·???????Credit Risk Analysis In Credit Risk Analysis based on accounting ratios and other financial variables, linear discriminant analysis was the first statistical method applied to systematically explain which firms entered bankruptcy vs.?survived.

·???????Product Sales Prediction: In Sales, discriminant analysis can be used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data.

·???????Medical studies: The main application of discriminant analysis in medicine is the assessment of severity state of a patient and prognosis of disease outcome. For example, during retrospective analysis, patients are divided into groups according to severity of disease - mild, moderate, and severe form. Then results of clinical and laboratory analyses are studied in order to reveal variables that are statistically different in studied groups. Using these variables, discriminant functions are built which help to objectively classify disease in a future patient into mild, moderate or severe form.

In this article, based on the four different variables, whether a patient is Omicron Positive or Negative is determined. They are: - Fever, Cough, Tiredness, Smell/Taste Loss which are independent variables. The dependent variable is Omicron – a binary variable (Positive/Negative).

By using LDA in R the following Confusion matrix is obtained: -

Interpretation of Confusion matrix

True Positive(TP): The actual result is Positive and the model is predicting them as Positive = 32

False Positive(FP): Actual result is Negative and the model is predicting them as Positive.=4

False Negative(FN): Actual result is Positive and the model is predicting them as Negative.=0

True Negative (TN): The actual result is Negative and the model is predicting them as Negative = 64

The accuracy of the model is calculated to be 96%