An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia

Algorithm 1

Pseudocode of the proposed methodology.

	Parameters: C1, C2, C3, C4, C5, C6, C7, PC1, PC2…PCn,
	Input: ∈ R^MxN
	Output: Detection Accuracy (Au).
	(1)  For each Ci, estimate
	(2)  While Au Optimized
	(3)  Apply PCA
	(4)  For n no. Of PCs : Fs’
	(5)  For n upto 3, do
	(6)  Split into X1, X2, and X3 where X1= 0.7 , X2 = 0.15 , and X3 = 0.15
	(7)  For Ci:
	train the model with each data point in X1.
	Test the model with data points X2
	Validate the model with data points X3
	(8)  Estimate Au
	(9)  Reduce the value of n: return to 6
	Return Accuracy.