Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.
Citation: Zaineb M. Alhakeem, Heba Hakim, Ola A. Hasan, Asif Ali Laghari, Awais Khan Jumani, Mohammed Nabil Jasm. Prediction of diabetic patients in Iraq using binary dragonfly algorithm with long-short term memory neural network[J]. AIMS Electronics and Electrical Engineering, 2023, 7(3): 217-230. doi: 10.3934/electreng.2023013
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Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.
Ostrowski proved the following interesting and useful integral inequality in 1938, see [18] and [15, page:468].
Theorem 1.1. Let f:I→R, where I⊆R is an interval, be a mapping differentiable in the interior I∘ of I and let a,b∈I∘ with a<b. If |f′(x)|≤M for all x∈[a,b], then the following inequality holds:
|f(x)−1b−a∫baf(t)dt|≤M(b−a)[14+(x−a+b2)2(b−a)2] | (1.1) |
for all x∈[a,b]. The constant 14 is the best possible in sense that it cannot be replaced by a smaller one.
This inequality gives an upper bound for the approximation of the integral average 1b−a∫baf(t)dt by the value of f(x) at point x∈[a,b]. In recent years, such inequalities were studied extensively by many researchers and numerous generalizations, extensions and variants of them appeared in a number of papers, see [1,2,10,11,19,20,21,22,23].
A function f:I⊆R→R is said to be convex (AA−convex) if the inequality
f(tx+(1−t)y)≤tf(x)+(1−t)f(y) |
holds for all x,y∈I and t∈[0,1].
In [4], Anderson et al. also defined generalized convexity as follows:
Definition 1.1. Let f:I→(0,∞) be continuous, where I is subinterval of (0,∞). Let M and N be any two Mean functions. We say f is MN-convex (concave) if
f(M(x,y))≤(≥)N(f(x),f(y)) |
for all x,y∈I.
Recall the definitions of AG−convex functions, GG−convex functions and GA− functions that are given in [16] by Niculescu:
The AG−convex functions (usually known as log−convex functions) are those functions f:I→(0,∞) for which
x,y∈I and λ∈[0,1]⟹f(λx+(1−λ)y)≤f(x)1−λf(y)λ, | (1.2) |
i.e., for which logf is convex.
The GG−convex functions (called in what follows multiplicatively convex functions) are those functions f:I→J (acting on subintervals of (0,∞)) such that
x,y∈I and λ∈[0,1]⟹f(x1−λyλ)≤f(x)1−λf(y)λ. | (1.3) |
The class of all GA−convex functions is constituted by all functions f:I→R (defined on subintervals of (0,∞)) for which
x,y∈I and λ∈[0,1]⟹f(x1−λyλ)≤(1−λ)f(x)+λf(y). | (1.4) |
The article organized three sections as follows: In the first section, some definitions an preliminaries for Riemann-Liouville and new fractional conformable integral operators are given. Also, some Ostrowski type results involving Riemann-Liouville fractional integrals are in this section. In the second section, an identity involving new fractional conformable integral operator is proved. Further, new Ostrowski type results involving fractional conformable integral operator are obtained by using some inequalities on established lemma and some well-known inequalities such that triangle inequality, Hölder inequality and power mean inequality. After the proof of theorems, it is pointed out that, in special cases, the results reduce the some results involving Riemann-Liouville fractional integrals given by Set in [27]. Finally, in the last chapter, some new results for AG-convex functions has obtained involving new fractional conformable integrals.
Let [a,b] (−∞<a<b<∞) be a finite interval on the real axis R. The Riemann-Liouville fractional integrals Jαa+f and Jαb−f of order α∈C (ℜ(α)>0) with a≥0 and b>0 are defined, respectively, by
Jαa+f(x):=1Γ(α)∫xa(x−t)α−1f(t)dt(x>a;ℜ(α)>0) | (1.5) |
and
Jαb−f(x):=1Γ(α)∫bx(t−x)α−1f(t)dt(x<b;ℜ(α)>0) | (1.6) |
where Γ(t)=∫∞0e−xxt−1dx is an Euler Gamma function.
We recall Beta function (see, e.g., [28, Section 1.1])
B(α,β)={∫10tα−1(1−t)β−1dt(ℜ(α)>0;ℜ(β)>0)Γ(α)Γ(β)Γ(α+β) (α,β∈C∖Z−0). | (1.7) |
and the incomplete gamma function, defined for real numbers a>0 and x≥0 by
Γ(a,x)=∫∞xe−tta−1dt. |
For more details and properties concerning the fractional integral operators (1.5) and (1.6), we refer the reader, for example, to the works [3,5,6,7,8,9,14,17] and the references therein. Also, several new and recent results of fractional derivatives can be found in the papers [29,30,31,32,33,34,35,36,37,38,39,40,41,42].
In [27], Set gave some Ostrowski type results involving Riemann-Liouville fractional integrals, as follows:
Lemma 1.1. Let f:[a,b]→R be a differentiable mapping on (a,b) with a<b. If f′∈L[a,b], then for all x∈[a,b] and α>0 we have:
(x−a)α+(b−x)αb−af(x)−Γ(α+1)b−a[Jαx−f(a)+Jαx+f(b)]=(x−a)α+1b−a∫10tαf′(tx+(1−t)a)dt−(b−x)α+1b−a∫10tαf′(tx+(1−t)b)dt |
where Γ(α) is Euler gamma function.
By using the above lemma, he obtained some new Ostrowski type results involving Riemann-Liouville fractional integral operators, which will generalized via new fractional integral operators in this paper.
Theorem 1.2. Let f:[a,b]⊂[0,∞)→R be a differentiable mapping on (a,b) with a<b such that f′∈L[a,b]. If |f′| is s−convex in the second sense on [a,b] for some fixed s∈(0,1] and |f′(x)|≤M, x∈[a,b], then the following inequality for fractional integrals with α>0 holds:
|(x−a)α+(b−x)αb−af(x)−Γ(α+1)b−a[Jαx−f(a)+Jαx+f(b)]|≤Mb−a(1+Γ(α+1)Γ(s+1)Γ(α+s+1))[(x−a)α+1+(b−x)α+1α+s+1] |
where Γ is Euler gamma function.
Theorem 1.3. Let f:[a,b]⊂[0,∞)→R be a differentiable mapping on (a,b) with a<b such that f′∈L[a,b]. If |f′|q is s−convex in the second sense on [a,b] for some fixed s∈(0,1],p,q>1 and |f′(x)|≤M, x∈[a,b], then the following inequality for fractional integrals holds:
|(x−a)α+(b−x)αb−af(x)−Γ(α+1)b−a[Jαx−f(a)+Jαx+f(b)]|≤M(1+pα)1p(2s+1)1q[(x−a)α+1+(b−x)α+1b−a] |
where 1p+1q=1, α>0 and Γ is Euler gamma function.
Theorem 1.4. Let f:[a,b]⊂[0,∞)→R be a differentiable mapping on (a,b) with a<b such that f′∈L[a,b]. If |f′|q is s−convex in the second sense on [a,b] for some fixed s∈(0,1],q≥1 and |f′(x)|≤M, x∈[a,b], then the following inequality for fractional integrals holds:
|(x−a)α+(b−x)αb−af(x)−Γ(α+1)b−a[Jαx−f(a)+Jαx+f(b)]|≤M(1+α)1−1q(1+Γ(α+1)Γ(s+1)Γ(α+s+1))1q[(x−a)α+1+(b−x)α+1b−a] |
where α>0 and Γ is Euler gamma function.
Theorem 1.5. Let f:[a,b]⊂[0,∞)→R be a differentiable mapping on (a,b) with a<b such that f′∈L[a,b]. If |f′|q is s−concave in the second sense on [a,b] for some fixed s∈(0,1],p,q>1, x∈[a,b], then the following inequality for fractional integrals holds:
|(x−a)α+(b−x)αb−af(x)−Γ(α+1)b−a[Jαx−f(a)+Jαx+f(b)]|≤2s−1q(1+pα)1p(b−a)[(x−a)α+1|f′(x+a2)|+(b−x)α+1|f′(b+x2)|] |
where 1p+1q=1, α>0 and Γ is Euler gamma function.
Some fractional integral operators generalize the some other fractional integrals, in special cases, as in the following integral operator. Jarad et. al. [13] has defined a new fractional integral operator. Also, they gave some properties and relations between the some other fractional integral operators, as Riemann-Liouville fractional integral, Hadamard fractional integrals, generalized fractional integral operators etc., with this operator.
Let β∈C,Re(β)>0, then the left and right sided fractional conformable integral operators has defined respectively, as follows;
βaJαf(x)=1Γ(β)∫xa((x−a)α−(t−a)αα)β−1f(t)(t−a)1−αdt; | (1.8) |
βJαbf(x)=1Γ(β)∫bx((b−x)α−(b−t)αα)β−1f(t)(b−t)1−αdt. | (1.9) |
The results presented here, being general, can be reduced to yield many relatively simple inequalities and identities for functions associated with certain fractional integral operators. For example, the case α=1 in the obtained results are found to yield the same results involving Riemann-Liouville fractional integrals, given before, in literatures. Further, getting more knowledge, see the paper given in [12]. Recently, some studies on this integral operators appeared in literature. Gözpınar [13] obtained Hermite-Hadamard type results for differentiable convex functions. Also, Set et. al. obtained some new results for quasi−convex, some different type convex functions and differentiable convex functions involving this new operator, see [24,25,26]. Motivating the new definition of fractional conformable integral operator and the studies given above, first aim of this study is obtaining new generalizations.
Lemma 2.1. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. Then the following equality for fractional conformable integrals holds:
(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]=(x−a)αβ+1b−a∫10(1−(1−t)αα)βf′(tx+(1−t)a)dt+(b−x)αβ+1b−a∫10(1−(1−t)αα)βf′(tx+(1−t)b)dt. |
where α,β>0 and Γ is Euler Gamma function.
Proof. Using the definition as in (1.8) and (1.9), integrating by parts and and changing variables with u=tx+(1−t)a and u=tx+(1−t)b in
I1=∫10(1−(1−t)αα)βf′(tx+(1−t)a)dt,I2=∫10(1−(1−t)αα)βf′(tx+(1−t)b)dt |
respectively, then we have
I1=∫10(1−(1−t)αα)βf′(tx+(1−t)a)dt=(1−(1−t)αα)βf(tx+(1−t)a)x−a|10−β∫10(1−(1−t)αα)β−1(1−t)α−1f(tx+(1−t)a)x−adt=f(x)αβ(x−a)−β∫xa(1−(x−ux−a)αα)β−1(x−ux−a)α−1f(u)x−adux−a=f(x)αβ(x−a)−β(x−a)αβ+1∫xa((x−a)α−(x−u)αα)β−1(x−u)α−1f(u)du=f(x)αβ(x−a)−Γ(β+1)(x−a)αβ+1βJαxf(a), |
similarly
I2=∫10(1−(1−t)αα)βf′(tx+(1−t)b)dt=−f(x)αβ(b−x)+Γ(β+1)(b−x)αβ+1βxJαf(b) |
By multiplying I1 with (x−a)αβ+1b−a and I2 with (b−x)αβ+1b−a we get desired result.
Remark 2.1. Taking α=1 in Lemma 2.1 is found to yield the same result as Lemma 1.1.
Theorem 2.1. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. If |f′| is convex on [a,b] and |f′(x)|≤M with x∈[a,b], then the following inequality for fractional conformable integrals holds:
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤Mαβ+1B(1α,β+1)[(x−a)αβ+1b−a+(b−x)αβ+1b−a] | (2.1) |
where α,β>0, B(x,y) and Γ are Euler beta and Euler gamma functions respectively.
Proof. From Lemma 2.1 we can write
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1b−a∫10(1−(1−t)αα)β|f′(tx+(1−t)a)|dt+(b−x)αβ+1b−a∫10(1−(1−t)αα)β|f′(tx+(1−t)b)|dt≤(x−a)αβ+1b−a[∫10(1−(1−t)αα)βt|f′(x)|dt+∫10(1−(1−t)αα)β(1−t)|f′(a)|dt]+(b−x)αβ+1b−a[∫10(1−(1−t)αα)βt|f′(x)|dt+∫10(1−(1−t)αα)β(1−t)|f′(b)|dt]. | (2.2) |
Notice that
∫10(1−(1−t)αα)βtdt=1αβ+1[B(1α,β+1)−B(2α,β+1)],∫10(1−(1−t)αα)β(1−t)dt=B(2α,β+1)αβ+1. | (2.3) |
Using the fact that, |f′(x)|≤M for x∈[a,b] and combining (2.3) with (2.2), we get desired result.
Remark 2.2. Taking α=1 in Theorem 3.1 and s=1 in Theorem 1.2 are found to yield the same results.
Theorem 2.2. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. If |f′|q is convex on [a,b], p,q>1 and |f′(x)|≤M with x∈[a,b], then the following inequality for fractional conformable integrals holds:
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤M[B(βp+1,1α)αβ+1]1p[(x−a)αβ+1b−a+(b−x)αβ+1b−a] | (2.4) |
where 1p+1q=1, α,β>0, B(x,y) and Γ are Euler beta and Euler gamma functions respectively.
Proof. By using Lemma 2.1, convexity of |f′|q and well-known Hölder's inequality, we have
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1b−a[(∫10(1−(1−t)αα)βp)1p(∫10|f′(tx+(1−t)a)|qdt)1q]+(b−x)αβ+1b−a[(∫10(1−(1−t)αα)βp)1p(∫10|f′(tx+(1−t)b)|qdt)1q]. | (2.5) |
Notice that, changing variables with x=1−(1−t)α, we get
∫10(1−(1−t)αα)βp=B(βp+1,1α)αβ+1. | (2.6) |
Since |f′|q is convex on [a,b] and |f′|q≤Mq, we can easily observe that,
∫10|f′(tx+(1−t)a)|qdt≤∫10t|f′(x)|qdt+∫10(1−t)|f′(a)|qdt≤Mq. | (2.7) |
As a consequence, combining the equality (2.6) and inequality (2.7) with the inequality (2.5), the desired result is obtained.
Remark 2.3. Taking α=1 in Theorem 3.2 and s=1 in Theorem 1.3 are found to yield the same results.
Theorem 2.3. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. If |f′|q is convex on [a,b], q≥1 and |f′(x)|≤M with x∈[a,b], then the following inequality for fractional conformable integrals holds:
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤Mαβ+1B(1α,β+1)[(x−a)αβ+1b−a+(b−x)αβ+1b−a] | (2.8) |
where α,β>0, B(x,y) and Γ are Euler Beta and Euler Gamma functions respectively.
Proof. By using Lemma 2.1, convexity of |f″|q and well-known power-mean inequality, we have
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1b−a(∫10(1−(1−t)αα)βdt)1−1q(∫10(1−(1−t)αα)β|f′(tx+(1−t)a)|qdt)1q+(b−x)αβ+1b−a(∫10(1−(1−t)αα)βdt)1−1q(∫10(1−(1−t)αα)β|f′(tx+(1−t)b)|qdt)1q. | (2.9) |
Since |f′|q is convex and |f′|q≤Mq, by using (2.3) we can easily observe that,
∫10(1−(1−t)αα)β|f′(tx+(1−t)a)|qdt≤∫10(1−(1−t)αα)β[t|f′(x)|q+(1−t)|f′(a)|q]dt≤Mqαβ+1B(1α,β+1). | (2.10) |
As a consequence,
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1b−a(1αβ+1B(1α,β+1))1−1q(Mqαβ+1B(1α,β+1))1q+(b−x)αβ+1b−a(1αβ+1B(1α,β+1))1−1q(Mqαβ+1B(1α,β+1))1q=Mαβ+1B(1α,β+1)[(x−a)αβ+1b−a+(b−x)αβ+1b−a]. | (2.11) |
This means that, the desired result is obtained.
Remark 2.4. Taking α=1 in Theorem 3.2 and s=1 in Theorem 1.4 are found to yield the same results.
Theorem 2.4. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. If |f′|q is concave on [a,b], p,q>1 and |f′(x)|≤M with x∈[a,b], then the following inequality for fractional conformable integrals holds:
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤[B(βp+1,1α)αβ+1]1p[(x−a)αβ+1b−a|f′(x+a2)|+(b−x)αβ+1b−a|f′(x+b2)|] | (2.12) |
where 1p+1q=1, α,β>0, B(x,y) and Γ are Euler Beta and Gamma functions respectively.
Proof. By using Lemma 2.1 and well-known Hölder's inequality, we have
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1b−a[(∫10(1−(1−t)αα)βp)1p(∫10|f′(tx+(1−t)a)|qdt)1q]+(b−x)αβ+1b−a[(∫10(1−(1−t)αα)βp)1p(∫10|f′(tx+(1−t)b)|qdt)1q]. | (2.13) |
Since |f″|q is concave, it can be easily observe that,
|f′(tx+(1−t)a)|qdt≤|f′(x+a2)|,|f′(tx+(1−t)b)|qdt≤|f′(b+x2)|. | (2.14) |
Notice that, changing variables with x=1−(1−t)α, as in (2.6), we get,
∫10(1−(1−t)αα)βp=B(βp+1,1α)αβ+1. | (2.15) |
As a consequence, substituting (2.14) and (2.15) in (2.13), the desired result is obtained.
Remark 2.5. Taking α=1 in Theorem 3.2 and s=1 in Theorem 1.5 are found to yield the same results.
Some new inequalities for AG-convex functions has obtained in this chapter. For the simplicity, we will denote |f′(x)||f′(a)|=ω and |f′(x)||f′(b)|=ψ.
Theorem 3.1. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. If |f′| is AG−convex on [a,b], then the following inequality for fractional conformable integrals holds:
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤|f′(a)|(x−a)αβ+1αβ(b−a)[ω−1lnω−(ωln−αβ−1(ω)(Γ(αβ+1)−Γ(αβ+1,lnω)))]+|f′(b)|(b−x)αβ+1αβ(b−a)[ψ−1lnψ−(ψln−αβ−1(ψ)(Γ(αβ+1)−Γ(αβ+1,lnψ)))] |
where α>0,β>1, Re(lnω)<0∧Re(lnψ)<0∧Re(αβ)>−1,B(x,y),Γ(x,y) and Γ are Euler Beta, Euler incomplete Gamma and Euler Gamma functions respectively.
Proof. From Lemma 2.1 and definition of AG−convexity, we have
(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]≤(x−a)αβ+1b−a∫10(1−(1−t)αα)β|f′(tx+(1−t)a)|dt+(b−x)αβ+1b−a∫10(1−(1−t)αα)β|f′(tx+(1−t)b)|dt≤(x−a)αβ+1b−a[∫10(1−(1−t)αα)β|f′(a)|(|f′(x)||f′(a)|)tdt]+(b−x)αβ+1b−a[∫10(1−(1−t)αα)β|f′(b)|(|f′(x)||f′(b)|)tdt]. | (3.1) |
By using the fact that |1−(1−t)α|β≤1−|1−t|αβ for α>0,β>1, we can write
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1αβ(b−a)[∫10(1−|1−t|αβ)|f′(a)|(|f′(x)||f′(a)|)tdt]+(b−x)αβ+1αβ(b−a)[∫10(1−|1−t|αβ)|f′(b)|(|f′(x)||f′(b)|)tdt]. |
By computing the above integrals, we get the desired result.
Theorem 3.2. Let f:[a,b]→R be a differentiable function on (a,b) with a<b and f′∈L[a,b]. If |f′|q is AG−convex on [a,b] and p,q>1, then the following inequality for fractional conformable integrals holds:
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(B(βp+1,1α)αβ+1)1p[|f′(a)|(x−a)αβ+1b−a(ωq−1qlnω)1q+|f′(b)|(b−x)αβ+1b−a(ψq−1qlnψ)1q]. |
where 1p+1q=1, α,β>0, B(x,y) and Γ are Euler beta and Euler gamma functions respectively.
Proof. By using Lemma 2.1, AG−convexity of |f′|q and well-known Hölder's inequality, we can write
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(x−a)αβ+1b−a[(∫10(1−(1−t)αα)βp)1p(|f′(a)|q∫10(|f′(x)||f′(a)|)qtdt)1q]+(b−x)αβ+1b−a[(∫10(1−(1−t)αα)βp)1p(|f′(b)|q∫10(|f′(x)||f′(b)|)qtdt)1q]. |
By a simple computation, one can obtain
|(x−a)αβ+(b−x)αβ(b−a)αβf(x)−Γ(β+1)b−a[βxJαf(b)+βJαxf(a)]|≤(B(βp+1,1α)αβ+1)1p×[|f′(a)|(x−a)αβ+1b−a(ωq−1qlnω)1q+|f′(b)|(b−x)αβ+1b−a(ψq−1qlnψ)1q]. |
This completes the proof.
Corollary 3.1. In our results, some new Ostrowski type inequalities can be derived by choosing |f′|≤M. We omit the details.
The authors declare that no conflicts of interest in this paper.
[1] | Iraq diabetes report 2000-2045. Available from: https://www.diabetesatlas.org/data/en/country/96/iq.html |
[2] |
Tigga NP, Garg S (2023) Speech Emotion Recognition for multiclass classification using Hybrid CNN-LSTM. International Journal of Microsystems and Iot 1: 9–17. https://doi.org/10.5281/zenodo.8158288 doi: 10.5281/zenodo.8158288
![]() |
[3] |
Jaber HA, Rashid MT (2019) HD-sEMG gestures recognition by SVM classifier for controlling prosthesis. Iraqi Journal of Computers, Communications, Control and System Engineering (IJCCCE) 19: 10–19. https://doi.org/10.33103/uot.ijccce.19.1.2 doi: 10.33103/uot.ijccce.19.1.2
![]() |
[4] |
Abgeena A, Garg S (2023) A novel convolution bi-directional gated recurrent unit neural network for emotion recognition in multichannel electroencephalogram signals. Technol Health Care 31: 1215–1234. https://doi.org/10.3233/THC-220458 doi: 10.3233/THC-220458
![]() |
[5] |
Mujumdar A, Vaidehi V (2019) Diabetes prediction using machine learning algorithms. Procedia Computer Science 165: 292–299. https://doi.org/10.1016/j.procs.2020.01.047 doi: 10.1016/j.procs.2020.01.047
![]() |
[6] |
Madan P, Singh V, Chaudhari V, Albagory Y, Dumka A, Singh R, et al. (2022) An optimization-based diabetes prediction model using cnn and bi-directional lstm in real-time environment. Applied Sciences 12: 3989. https://doi.org/10.3390/app12083989 doi: 10.3390/app12083989
![]() |
[7] |
Chang V, Bailey J, Xu QA, Sun Z (2023) Pima indians diabetes mellitus classification based on machine learning (ml) algorithms. Neural Computing and Applications 36: 16157–16173. https://doi.org/10.1007/s00521-022-07049-z doi: 10.1007/s00521-022-07049-z
![]() |
[8] | Noori NA, Yassin AA (2021) A comparative analysis for diabetic prediction based on machine learning techniques. Journal of Basrah Researches (Sciences) 47. |
[9] |
Ahamed BS, Arya MS, Nancy AO (2022) Prediction of type-2 diabetes mellitus disease using machine learning classifiers and techniques. Frontiers in Computer Science 4: 835242. https://doi.org/10.1155/2022/9220560 doi: 10.1155/2022/9220560
![]() |
[10] | Butt UM, Letchmunan S, Ali M, Hassan FH, Baqir A, Sherazi H (2021) Machine learning based diabetes classification and prediction for healthcare applications. Journal of healthcare engineering 2021. https://doi.org/10.1155/2021/9930985 |
[11] |
Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H (2018) Predicting diabetes mellitus with machine learning techniques. Frontiers in genetics 9: 515. https://doi.org/10.3389/fgene.2018.00515 doi: 10.3389/fgene.2018.00515
![]() |
[12] |
Naz H, Ahuja S (2020) Deep learning approach for diabetes prediction using pima indian dataset. Journal of Diabetes and Metabolic Disorders 19: 391–403. https://doi.org/10.1007/s40200-020-00520-5 doi: 10.1007/s40200-020-00520-5
![]() |
[13] |
Olisah CC, Smith L, Smith M (2022) Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Comput Meth Prog Bio 220: 106773. https://doi.org/10.1016/j.cmpb.2022.106773 doi: 10.1016/j.cmpb.2022.106773
![]() |
[14] | Rashid A (2020) Diabetes dataset, Mendeley Data. |
[15] |
Mirjalili S (2015) Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27: 1053–1073. https://doi.org/10.1007/s00521-015-1920-1 doi: 10.1007/s00521-015-1920-1
![]() |
[16] | Mafarja MM, Eleyan D, Jaber I, Hammouri A, Mirjalili S (2017) Binary Dragonfly Algorithm for Feature Selection. 2017 International Conference on New Trends in Computing Sciences (ICTCS), 12–17. |
[17] | Alhakeem ZM, Ali RS (2019) Fast channel selection method using crow search algorithm. Proceedings of the International Conference on Information and Communication Technology, 210–214. https://doi.org/10.1145/3321289.3321309 |
[18] |
Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Computation 9: 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 doi: 10.1162/neco.1997.9.8.1735
![]() |
[19] | Dalianis H (2018) Evaluation Metrics and Evaluation, Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-78503-5_6 |
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