An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

Algorithm 1

Heart disease classification; take input features, preprocessing, feature selection, and classification.

(1)	Input: K, f
(2)	Output: K Classification with matrix evaluation
(3)	if Dk ≠ 0 then
(4)	procedure (features (f), K)
(5)	f [V] nk+1/k ←Null
(6)	z = (x - u)/s ←
(7)	dataset fr [0, 1] ← M
(8)	end procedure
(9)	Procedure Feature Extraction (fk)
(10)	(Lf1, Lf2, Lf3……Lfn) ← Lk
(11)	(Af1, Af2, Af3……Afn) ← Ak
(12)	(Mf1, Mf2, Mf3……Mfn) ← Mk
(13)	(Vf1, Vf2, Vf3……. Vfn) ← Vk
(14)	(Mf1, Mf2, Mf3……. Mfn) ← Mk
(15)	Return (f1, f2, f3……fn) ← k
(16)	end procedure
(17)	procedure C ((f1, f2, f3……fn), Gk)
(18)	M ⇐ Tk ((f1, f2, f3……fn), Gk)
(19)	Pk ⇐ TTk (M, (f1, f2, f3……fn))
(20)	Cr ⇐ matrix (Pk, Gk)
(21)	return Pk
(22)	end procedure
(23)	else
(24)	Dk = 0 ← empty
(25)	end if
(26)	until: All the features (K) are Classified(C)
(27)	Exit