# An Optimized Framework for Diabetes Mellitus Diagnosis Using Grid Search Based Support Vector Machine

S. Amutha, J. Raja Sekar

► To cite this version:

S. Amutha, J. Raja Sekar. An Optimized Framework for Diabetes Mellitus Diagnosis Using Grid Search Based Support Vector Machine. 7th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Jan 2023, Chennai, India. pp.153-167, 10.1007/978-3-031-39811-7_13. hal-05363227

HAL Id: hal-05363227

https://inria.hal.science/hal-05363227v1

Submitted on 13 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
<figure><img src="image_3.png" /></figure>

# An Optimized Framework for Diabetes Mellitus Diagnosis Using Grid Search Based Support Vector Machine

S. Amutha(✉) and J. Raja Sekar

Department of Computer Science and Engineering, Mepco Schlenk Engineering College
(Autonomous), Sivakasi, Tamilnadu, India
{amuthavasan,jrsekar}@mepcoeng.ac.in

**Abstract.** In this digital era, the people are living in a sophisticated environment and hence they are not exposed to healthy life style. Hence, people are prone to more diseases because of their unhealthy lifestyle and there are a greater number of diseases associated with this. Among them Diabetic Mellitus has become an epidemic because of its dominant prevalence. Though, there are more contributions in diabetic diagnosis, still there is a scope for improvement. This work focuses on the implementation of a framework for diabetic disease diagnosis. The Binarized Whale optimization Algorithm and Binarized grey wolf optimization Algorithm are used for optimal feature selection. The Grid Search based Support Vector Machine Classifier is used for prediction model for predicting diabetic disease. The grid search technique is used here for tuning the hyper parameter for improving the classification accuracy. The developed framework achieves the accuracy of 98.71% on the Vanderbilt diabetes data.

**Keywords:** Binarized Whale optimization · Binarized grey wolf optimization · Diabetic mellitus diagnosis · Grid Search · Hyper parameter tuning

## 1 Introduction

After 1990, there has been a slight increase in the prevalence of diabetes in India; this increase has accelerated since 2000. Diabetes prevalence is expected to double globally from 171 million cases in 2000 to 366 million cases in 2030, with India experiencing the largest increase [2].

As per the statistics [3] taken by International Diabetic Federation during 2019, around 463 million people are there globally with diabetics and it is expected to be doubled by 2030. Diabetes is a metabolic disorder that develops when blood sugar levels rise above a certain point. It can be due to inadequate insulin secretion or a lack of insulin use by the body for the production of energy. The undiagnosed high blood sugar levels might result in a variety of consequences. There are four different types of diabetics, including Type I Diabetics, who develop the disease as a result of damage to the cells that make insulin in the pancreas; When the body is unable to use insulin, type
II diabetes develops; Prediabetics are those who have blood sugar levels that are higher than average but not as high as Type I or Type II levels; The type of diabetes that develops due of pregnancy is known as gestational diabetes. World Health Organization made an estimation to interpret that the diabetics is the eighth leading cause of death and it is associated with complications like heart disease, stroke and renal disease. This escalates the significance of diabetic diagnosis and complication prediction at early stage so that diabetic people may be alerted and their lives can be extended.

## 2 Literature Works

The diabetic diagnosis was implemented by different researchers using various machine learning classifiers and optimizers [14]. The contributions are discussed elaborately here in this section. Most of the research contributions are exercised on the PIMA Indian dataset. PIMA Indian dataset is a public diabetes dataset which has 768 number of instances. The input attributes no.of times pregnant, 2h PGC, Systolic and diastolic BP, skin thickness, 2-h serum insulin, BMI, pedigree function, and Age. Nilashi et. al. [4] (2017) proposed a system for enhancing the accuracy of the diabetes disease prediction. The authors used self-organizing maps for grouping the experimental data into different clusters. The clusters could then be fed to the dimensionality reduction. The principal component analysis was used to reduce the feature dimensionality of the input features. The dataset with the selected features were classified using neural network classifier. Using a random forest technique and an artificial neural network model, Alam et al. (2019) built a model. Principal Component Analysis was used to lessen the dataset's dimensionality. The authors used the Apriori technique to determine whether input and the goal variable were related. The rules were constructed by including The BMI and glucose levels since the authors inferred that they had a substantial correlation. However, other important factors including family history and physical inactivity were not taken into account. Birjais et. al (2019) developed a system for predicting future diabetes risks on PIMA Indian dataset. The authors exploited the machine learning classifiers such as gradient boosting, logistic regression and naïve Bayesian algorithms for classifying the diabetes instances.

Among them, gradient boosting technique gave an accuracy of 86%. An optimized model was proposed by Massaro et. al. [5] (2019) for diabetes prediction using long short term memory based data mining technique. A model for predicting diabetes risk was proposed by Fazakis et al. [6] (2021). They applied the model using the English Longitudinal Study of Aging (ELSA) database. The significant traits were selected using the LASSO feature selection method. The weighted voting ensemble method was used to categorize the data successfully. To add to the diabetes disease prediction model, Tiwari et al. [7] (2021) applied machine learning algorithms and a well-known feature selection approach. The features are chosen using the RF technique and recursive feature selection. The missing values are replaced by the authors using a mean value replacement strategy. In order to predict diseases, the XGBoost algorithm was utilized. The accuracy of the proposed algorithm, which is 78.97%, could be increased. A prediction model was developed by Roy et al. (2021) and it focused on the imputation of missing values using k nearest neighbour imputation, median values replacement. To test the efficiency of
different imputation techniques, the system was validated with various machine learning
classifiers. The better results were achieved using neural network classifiers. Annamalai
R et. al. [8] (2021) proposed a diabetes mellitus diagnosis framework and they have
identified the severity level of the disease. The authors used switching mid value based
morphological filtering for data preprocessing. The system was implemented in two
phases namely classification phase and intensity range of diabetes mellitus estimation
phase. In classification phase, the optimal weight based deep artificial neural network was
used for classification of the disease. During severity level estimation phase, the Great
circle clustering based on distance calculation was exploited to compute the severity of
the diabetes mellitus.

A diabetic disease prediction system was proposed by Beghriche et. al. [9] (2021) using deep neural network. The dataset is normalized using standard normalization. The classifiers such as RF, regression based classification, SVM and Extreme boosting were used for diabetes prediction. The different machine learning models were tested with hyper parameter tuning. Among all, the deep neural network gave the improved accuracy. Abdollahi et. al. [10] (2022) developed a prediction model using stacking based ensemble model for diabetes prediction. Stacked generalization model combined different machine learning classifiers at two different levels. The levels are basic level or zero level and learners level. The model facilitates the creation of input for next layer.

Olisah et. al. [11] (2022) developed a system for diabetic diagnosis on PIMA Indian dataset and LMCH dataset using machine learning classifiers. The system used Spearman correlation coefficient for feature selection and Polynomial regression for missing value imputation. The classification was experimented using Random Forest classifier, Support Vector Machine classifier and deep neural networks. The feed forward deep neural network along with non-linear activation function was exploited for transformation of input. Khan et. al. [12] (2022) designed a hybrid and improved diabetes disease prediction model. The authors used correlation metrics such as correlation score, Fscore and particle swarm optimization for selecting the optimal features. Then the selected features were classified using neural network.

The feature selection could still be improved for better results. A technique for diagnosing diabetes was developed by Saxena et al. [13] in 2022. For feature selection, the authors employed conditional entropy, information gain, and correlation coefficient. For disease prediction, they used machine learning techniques including MLP, RF and KNN, and decision tree methods. The existing literatures are exploited on the PIMA Indian dataset. The proposed work is tested using the data set [1] for training and testing since it can be further exploited for future complication predictions.

## 3 Methodology

The diabetic diagnosis implemented using the framework which consists of the data pre-
processing, feature selection and classification. The flow of the proposed framework is
pictorially depicted in Fig. 1. The features in the Vanderbilt dataset is pre-processed. The
pre-processed data is further processed by the optimizer module. The optimizer module
is meant for selecting the optimal attributes for achieving better results of classification.
The selection of feature is implemented using the following optimization algorithms
namely Binarized Whale optimization Algorithm and Binarized Grey Wolf optimization Algorithm.

The optimal features are the list of features which contributes prediction of diabetes mellitus. The two optimizer yields two separate set of optimal features. The two sets are given to Grid search based SVM for further classification. The grid search tunes the algorithm specific parameters of SVM classifier. The best parameter values that give improved accuracy are used as parameters for SVM classifier and it predicts diabetic mellitus.

<figure><img src="image_4.png" /><figcaption>Fig. 1. System Diagram of the Diabetes Mellitus prediction</figcaption></figure>

## 3.1 Data Preprocessing

The diabetes dataset [1] has 390 patient records. It has 15 input features such as cholesterol, glucose and blood pressure and so on. The dataset is normalized using the Standard Normal Distribution. It eliminates the mean and scales to unit variance on the samples in the training set to standardize features using column summary statistics.

## 3.2 Feature Selection

The optimal feature selection is the necessary step for disease diagnosis. The significant and essential clinical parameters will give better prediction. There are different feature selection strategies. The major types of feature selection strategies are supervised and unsupervised feature selection. Unsupervised methods are meant for unlabeled data. The supervised feature selection strategies are Filter based feature selection, forward and backward attribute selection and hybrid attribute selection.

The filter based methods works by computing the statistical measurements for finding the relevance of the corresponding attribute to the target variable. The statistical values like information gain, mutual information, chi-square test, Pearson correlation coefficient, ANOVA coefficient, Fisher's score are used for feature importance calculation. Wrapper based methods selects the features in different order for finding the best set. Based on this, they come in three types such as forward feature selection, backward
feature selection, brute force feature selection. The forward feature selection is imple-
mented in iterative fashion for feature selection. It starts with one feature at a time and
evaluates the performance and keeps adding the features one at a time. Whenever a new
feature is added, performance is computed. The same process is repeated until feature
inclusion does not get better the efficiency. The backward feature selection starts with
the entire dataset and then keeps on eliminating the features. Them the performance is
computed after the removal of attributes. It is done in iterative fashion. The same pro-
cess is repeated until feature deletion does not improve the performance. The brute force
method works exhaustively works with all combination of attributes and evaluate the
performance with those attributes. Embedded feature selection methods combine both
forward and backward feature selection strategies.

3.3 Bio-Inspired Computing for Feature Selection

The bio – inspired computing is a technique for building computer systems that infers ideas from the biological world. It is based on the evolution of the natural processes from the biological world. The bio inspired computing algorithms mimic the behavior of the birds, animals, and insects and so on. Some of the bio inspired computing algorithms are Genetic Algorithms, Particle Swarm Optimization, Ant colony optimization, Grey wolf optimization, Whale optimization. Among the above algorithms, the proposed system is implemented using grey wolf and whale optimization algorithms. The algorithms are modified to have BGWoA and BWoA to get the optimal solutions.

3.4 Binarized Whale Optimization Algorithm

Whales are considered to be the biggest mammal and the body parts are humpback,
killer, blue and finback. The Whale Optimization Algorithm (WOA) is a metaheuristic
optimization algorithm inspired by nature that mimics the hunting behavior of humpback
whales. The bubble net search characteristic of whales is exploited for optimization.
Bubble-net feeding explains how humpback whales" forage. Near the water's surface,
humpback whales frequently pursue krill or small fish for food. Navigating through
many phases, such as the encircling the solution, exploitation, and exploration phases,
yields the solution.

* Initial Population

An initial population (solution space) made up of a random dataset with random
features is then evaluated using a fitness function. Initial random features and its pro-
cessing are depicted in the Fig. 2. The initial a random population is selected and then
the algorithmic parameters are calculated to arrive at the next best solution. The process
is repeated until an optimal solution is repeated.

The location of the prey searched by the whales in true scenario is correlated with
best solution. The whale's ability to navigate its way to its prey is connected with the
solution in the positioned solution space. Based on the navigation in various phases, the
position of the whale will be updated each time are encircling of the solution, attacking
the solution and exploration of solution for optimal results.
<figure><img src="image_5.png" /><figcaption>Fig. 2. Flow of the Optimizer</figcaption></figure>

The optimal solution is unknown and it needs to be explored using WOA. The algorithm considers an initial random solution as the current best or the current solution may be nearer to the best solution. Once a solution vector is determined as the best search solution vector, position of the other solution vector will be updated based on the current solution vector. As per the search path of the whales, the solution may be explored either in the shrinking path or spiral path. There are 50 – 50 chances for the whale to move either in shrinking path or spiral path. The working of the proposed binarized whale optimization algorithm in shrinking path is realized by the following equations:

$$ \check{D} = |\hat{C} \cdot T_{best} - (t)| \quad (1) $$

$$ T(t + 1) = T(t) - \check{D} \quad (2) $$

T(t) – is the location vector of the solution

$$ \check{A} = 2 * r_1 * a - a \quad (3) $$

$$ \hat{C} = 2 * r_2 \quad (4) $$

where $\check{A}$ and $\hat{C}$ are vectors of position update coefficients that are specified by Eqs. 3 and 4. The solution which is obtained in the spiral path is visualized mathematically as follows.

$$ T(t + 1) = \text{Dist}' \cdot e^{bl} \cdot \cos(2\pi l) + T_{\text{best}} \text{ where } \text{Dist}' = T_{\text{best}} - T(t) \quad (5) $$

where ‘b’ is the constant which defines the shape of the logarithmic spiral path, ‘l’ – value randomly lies between –1 & 1 and ‘Dist’ is the distance from the current solution to the best solution. The solution is obtained using the Eq. 3.6 in the spiral path whereas the solution is obtained using the Eq. 3.3 in the shrinking path. The best solution is obtained by selecting the attributes (in binarized form) using the Eq. 7

$$ T_{\text{next}} = T_{\text{rand}} - \check{A} * \hat{C} \quad (6) $$
$$ \mathrm{sig}(T_{\mathrm{next}}) = e^{-10*((T_{\mathrm{next}}) - 0.5)} \quad (7) $$

where $\Psi_{next}$ is computed using the Eq. 6. The algorithm for the proposed bina rized
whale optimization algorithm is given as follows:

### Proposed Binarized Whale Optimization Algorithm

**Algorithm 1:** Binary Whale Optimization Optimization Input: pop_size,size,X,Y,c,alpha,epoch,p

Output : $\Psi_{best}$

begin

pop ← populate Solutionspace

(pop_size,size) ← computeOptimalSolution solu tion, pop_size, X, Y)

$r_1$ and $r_2$ – random numbers between 0 and 1

$\Psi$ – fitness value of the best solution i ← 0

while (i < epoch)

begin

for each solution in population pop

Generate random numbers $r_1$ and $r_2$ in the range[0,1] for Coefficient vector

calculationCompute Coefficient Vectors $\bar{A}_1$ and $\bar{C}_1$ as in equations 3 and 4

else if (p >= 0.5)

Update the position of the current feature as solution as in equation

5

If $\Psi_{next} > \Psi_{t}$

$\Psi_{best} = \Psi_{next}$

End for

i ← i + 1

end while

return $\Psi_{best}$

end

## 3.5 Binarized Grey Wolf Optimization

Optimization using the behavior of Grey wolves is one of the bio-inspired computing algorithms. It imitates the groups, which range in size from 5 to 12. The types of are Alpha, Beta, Gamma and Omega. The dominant breed of all other type wolves is Alpha. The role of other wolves is to accept and follow the Alpha wolves. Beta wolf will assist Alpha wolf to reach the prey. Alpha and Beta will command the next dominant wolf, known as Delta. Omega, the least aggressive wolf, can only successfully capture its prey. The three phases of grey wolf hunting are making a strategy to approach the prey, surrounding the prey safe without making it to move and reach the prey. The social hierarchy of the wolves is alpha, beta, gamma and then finally omega.

The way wolves navigate to find their prey is related to the solution in the posited solution space. Based on the navigation in various phases, the position of the whale will be updated each time and they are Dominance hierarchy, Encircling of solution in solution space ad finding optimal solution.

$\Psi\alpha$ is the best of other three solutions. The next better solutions are $\Psi\beta$ and $\Psi\delta$. The solution $\Psi\omega$ adapts the other three solutions and it is less dominant of other solutions. The best solution is computed as in the Eqs. 8 and 9. The solution space is the set of features selected for evaluation of fitness function. The optimal solution (i.e. the optimal features) are found by the search agents $\Psi\alpha, \Psi\beta, \Psi\delta$ and $\Psi\omega$.

$$ \tilde{D} = |\hat{C} \cdot T_{best} - (t)| \quad (8) $$
$$T(t + 1) = T(t) - \Delta t \qquad (9)$$

Δt and Ĉ are coefficient vectors for updating the position of the solution in the solution space and are computed using the Eqs. 10 and 11

$$\Delta t = 2 * r_1 * a - a \qquad (10)$$

$$\hat{C} = 2 * r_2 \qquad (11)$$

Δt is a vector of values generated randomly within the range of -2*a and 2*a. The value of "a" is decremented from 2 to 0 linearly. The random numbers r1 and r2 are generated. The hunting behavior is described by the Eq. 12, 13 and 14. The update of the new best solutions are updated using the values of Ψ1, Ψ2 and Ψ3. The next best set of features is the vector represented by Ψ1. The other solution vectors Ψ2 and Ψ3 know the location of the best solutions. The best solution position is mathematically represented as in the Eq. 18. The Ψ1, Ψ2 and Ψ3 are the first, second and third best solution vectors respectively and then they will get updated during the iterations.

$$\Delta t_a = |\hat{C} \cdot T_a - T(t)| \qquad (12)$$

$$\Delta t_{\beta} = |\hat{C} \cdot T_{\beta} - T(t)| \qquad (13)$$

$$\Delta t_{\delta} = |\hat{C} \cdot T_{\delta} - T(t)| \qquad (14)$$

The three solutions vectors for optimization are computed as follows.

$$T_1 = T_a - A_1 \Delta t_a \qquad (15)$$

$$T_2 = T_{\beta} - A_2 \Delta t_{\beta} \qquad (16)$$

$$T_3 = T_{\delta} - A_1 \Delta t_{\delta} \qquad (17)$$

$$T_{(t+1)} = \frac{T_1 + T_2 + T_3}{3} \qquad (18)$$
**Algorithm 2. BinaryGrey Wolf optimization Algorithm**

Input: `pop_size`, `size`, `X`, `Y`

Output: `solution_{best}`

begin

solution ← populate_solutionspace(pop_size, size)

solution_{best} ← computeOptimalSolution solut（ion，pop_size, X, Y)

//Let $\Psi$ - fitness value of the best

solution<sub>i</sub> ← []

while(i< epoch)

for each solution inpopulationpop

r1, r2 ← randomGen(0,1)

ComputeCoefficientVectorsÃ andC̃

Compute the fitness of the three search solution spaces $\Psi_a, \Psi_b$ and $\Psi_d$

//Let $\Psi_a$ ← First best solution

/

//$\Psi_b$ ← Second best solution

/

//$\Psi_d$ ← Third best solution

Compute $\Psi_{(next)}$

Update $\Psi_{(next)}$

Update the position of the current feature as solution

Update the coefficient vectors

Update the values of $\Psi_a, \Psi_b$ and $\Psi_d$

Compute the three solution agents of $\Psi_1, \Psi_2$ and $\Psi_3$

$\Psi_{next} \leftarrow mean(\Psi_1, \Psi_2, \Psi_3)$

t ← random[0,1]

//Binarizationofattributes

ifbinarizeAttributes($\Psi_{(next)}$) > = t

solution<sub>i</sub> ← concatenate solut（ion<sub>i</sub> + 1)

else

solution<sub>i</sub> ← concatenate solut（ion<sub>i</sub> + 0)

solution_{best} ← computeOptimalSolutionsolut（ion_{best}, pop_size, X, Y)

i ← i + 1

endFor

endWhile

return solution_{best}

end

## 3.6 Classifier

The optimal features which are selected from the optimizer are given to the classifier. The features selected from the diabetic dataset are best classified using the Support Vector Machine and the grid search technique. The hyper parameters are optimized using grid search technique.

**Parallel Hyperplane 1:** The points above or on this hyperplane are categorised as having the label 1, and weight * x - bias = 1. (i.e. the patients who have the diabetics).

**Parallel Hyperplane 2:** The points above or on this hyperplane are classed with the label 0 (i.e., the patients who do not have diabetes), and weight * x - bias = - 1.

### • Grid Search

The support vector machine classifier uses the following parameters for classification. The parameters are tuned for getting improved accuracy. They are regularization parameter, kernel type, degree of kernel, kernel coefficient value, and probability estimate enable.

The above parameters are the hyper parameters for the SVM classifier. The tuning of the values so that SVM yields the improved accuracy is done with the help of grid search technique. Grid search is a tuning technique that seeks the best hyper parameter values for the SVM classifier. Exhaustive combinations of all parameters are experimented and evaluated using the fitness function. In order to find the best value for the parameters, all possible combinations are experimented as per the Algorithm 3.
## Sensitivity

The ability of the classifier to accurately anticipate positive cases is measured by its sensitivity. It displays how precise it is at foreseeing cases of true positives. The algorithm can correctly predict all situations in which there isn't diabetes mellitus disease if the sensitivity is 100%. It is represented by the Eq. 21.

$$Sensitivity = \frac{\eta_{tp}}{\eta_{tp} + \eta_{fn}} \qquad (20)$$

## Specificity

Specificity is determined how accurately a classifier predicts negative situations. It shows how accurately it predicts the actual negative results. If the algorithm has 100% specificity, it can anticipate every instance of diabetes mellitus disease. The Eq. 22 is used to calculate it.

$$Specificity = \frac{\eta_{tn}}{\eta_{tn} + \eta_{fp}} \qquad (22)$$

## 4.2 Dataset Description

The diabetes dataset from [1] is chosen for diabetes prediction. The file is pre-processed, and cases for those whose HbA1C is absent are excluded. Out of 390 patients, 60 were found to have diabetes. The dataset has 390 records, and there are 15 input attributes. There are 228 female samples and 162 male samples in all. Table 1 provides a description of the dataset's list of features. The system is implemented with Anaconda navigator and python as an implementation language.

The features are represented using the notation f1,f2,f15 in the rest of the chapter as given in Table 1. The normal range of values for the clinical parameters of the dataset is given in Table 2.

## 4.3 Features Selection

There are 15 input features in the dataset. The Binarized whale and grey wolf optimizers are used for feature selection. The results of both of the algorithms are compared. The features which contributed the best fitness value are the optimal features. All of the feature sets are given to the classifier and the results are compared.

The features extracted out of the optimizers are listed in Table 3. The set of features selected by the Binarized whale optimization and Binarized grey wolf optimization algorithms are listed in Table 3. The Table 3(a) shows the performances of BWoA and Table 3(b) reveals the performance of the BGWoA.

* BWoA: The accuracy of the classifier is improved when the features { f3, f4, f10, f11, f13 } (Glucose, HDL Cholesterol, BMI, Systolic Blood Pressure and Waist) are selected as key contributors for the classification
<table><caption>**Table 1.** Parameters in the Dataset</caption><thead><tr><td>Feature Number</td><td>Parameter</td><td>Description</td></tr></thead><tbody><tr><td>f1</td><td>Patient ID</td><td>Unique Identification number of the patient</td></tr><tr><td>f2</td><td>Total Cholesterol</td><td>Total cholesterol</td></tr><tr><td>f3</td><td>Glucose</td><td>Fasting blood sugar</td></tr><tr><td>f4</td><td>HDL</td><td>HDL or good cholesterol</td></tr><tr><td>f5</td><td>Chol/HDL</td><td>Ratio of total cholesterol to good cholesterol</td></tr><tr><td>f6</td><td>Age</td><td>African and Americans Adult</td></tr><tr><td>f7</td><td>Gender</td><td>Population which has 162 males, 228 females</td></tr><tr><td>f8</td><td>Height</td><td>Height of the patient measures in inches</td></tr><tr><td>f9</td><td>Weight</td><td>Weight of the patient in pounds (lbs)</td></tr><tr><td>f10</td><td>BMI</td><td>Body Mass Index</td></tr><tr><td>f11</td><td>Systolic Blood Pressure</td><td>The upper level of BP</td></tr><tr><td>f12</td><td>Diastolic Blood Pressure</td><td>The lower level of BP</td></tr><tr><td>f13</td><td>Waist</td><td>Waist Circumference measured in inches</td></tr><tr><td>f14</td><td>Hip</td><td>Hip size measured in inches</td></tr><tr><td>f15</td><td>Waist/hip</td><td>Ratio of waist to hip ratio. The Ratio contributes significantly for diabetic prediction</td></tr><tr><td>f16</td><td>Diabetes</td><td>Target Variable</td></tr></tbody></table>

<table><caption>**Table 2.** Normal Range of values for Parameters in the Dataset</caption><thead><tr><td>Parameter</td><td>Normal Range</td></tr></thead><tbody><tr><td>Total Cholesterol</td><td>Less than 170 mg/dL</td></tr><tr><td>Glucose</td><td>Fasting Blood Glucose 70 – 100 mg/dl<br>Post Prandial Blood Glucose – less than 140 mg/dl</td></tr><tr><td>HDL</td><td>More than 45 mg/dL</td></tr><tr><td>BMI</td><td>18.5 – 24.9</td></tr><tr><td>Systolic BP</td><td>Less than 120</td></tr><tr><td>Diastolic BP</td><td>Less than 80</td></tr></tbody></table>

* BGWoA: The accuracy of the classifier is improved when the features {f2,f3,f4,f9,f10,f12, f15} (Total Cholesterol, Glucose, HDL, Glucose, HDL Cholesterol, BMI, Diastolic Blood Pressure and Waist) are selected as key contributors for the classification

The support vector machine classifier is experimented with the help of the input parameters. The parameters for SVM classifier are tuned using the grid search algorithm.
<table><caption>**Table 3.** Performance of the Optimizer (a) Binarized Whale Optimizer (b) Binarized Grey Wolf Optimizer</caption><thead><tr><th colspan="2">(a)</th><th colspan="2">(b)</th></tr><tr><th>Feature set</th><th>Features</th><th>Feature Set</th><th>Features</th></tr></thead><tbody><tr><td>F1</td><td>f3, f5, f6, f7, f8, f9</td><td>F1</td><td>f2, f3, f6, f7, f8, f9, f10, f11, f12, f13, f14, f15</td></tr><tr><td>F2</td><td>f2, f3, f4, f11, f12</td><td>F2</td><td>f3, f7, f8, f9, f10, f13, f14, f15</td></tr><tr><td><b>F3</b></td><td><b>f3, f4, f10, f11, f13</b></td><td><b>F3</b></td><td><b>f2, f3, f4, f9, f10, f12, f15</b></td></tr><tr><td>F4</td><td>f3, f8, f9, f10, f15</td><td>F4</td><td>f2, f3, f5, f7, f8, f10, f11, f14</td></tr><tr><td>F5</td><td>f3, f8, f9, f10, f15</td><td>F5</td><td>f2, f3, f8, f10, f11, f14</td></tr><tr><td>F6</td><td>f3, f8, f9, f10, f15</td><td>F6</td><td>f3, f4, f6, f7, f8, f9, f12, f13, f14</td></tr><tr><td>F7</td><td>f3, f8, f9, f10, f15</td><td>F7</td><td>f2, f3, f5, f11, f12, f15</td></tr><tr><td>F8</td><td>f3, f8, f9, f10, f15</td><td>F8</td><td>f3, f5, f6, f7, f9, f11, f12, f13, f15</td></tr></tbody></table>

<table><caption>**Table 4.** Performance of the Optimizers</caption><thead><tr><th>Optimization Algorithms</th><th>Optimal Features</th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th></tr></thead><tbody><tr><td rowspan="3">BWoA</td><td>f2, f3, f4, f10, f11, f12</td><td>89.74</td><td>89</td><td>98.94</td></tr><tr><td>f3, f4, f10, f11, f12, f13</td><td>90.59</td><td>65.45</td><td>95.64</td></tr><tr><td>f3, f4, f9, f10, f11, f12, f13, f14, f15</td><td>97.43</td><td>95.75</td><td>96.34</td></tr><tr><td rowspan="3">BGWoA</td><td>f2, f3, f6, f7, f8, f9, f10, f11, f12, f13, f14, f15</td><td>90.59</td><td>64.45</td><td>96.75</td></tr><tr><td>f2, f3, f8, f9, f10, f15</td><td>91.45</td><td>63.63</td><td>97.89</td></tr><tr><td>f2, f3, f4, f9, f10, f12, f15</td><td><b>98.71</b></td><td><b>89.74</b></td><td><b>89.74</b></td></tr></tbody></table>

The different combinations of regularization parameter with the values C ← {0.1, 1, 10, 100, 1000}, Kernel type ← “linear”, “poly”, “rbf”, “sigmoid” Kernel coefficient ← {1, 0.1, 0.01, 0.001, 0.0001} are set and evaluated for the optimal classification results.

Results shown in Table 4 gives the quantitative values of the optimizer with grid search based support vector machine classifier.

It is observed that the best results are observed when the features are selected using binarized grey wolf optimizer and the classification is done using Support vector Machine with the tuned parameters values Reg. Parameter, kernel coeff., kernel) as (1000, 1, linear). The performance of the optimizer is depicted in the Fig. 3. It is obvious that the results of the Binarized Grey Wolf optimizer Algorithm outperform the results of the Binarized Whale optimization Algorithm.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Performance of the Optimizers. (a) Binarized Whale Optimizer (b) Binarized Grey Wolf Optimizer</figcaption></figure>

## 5 Conclusion

The early diagnosis of the diabetes mellitus becomes inevitable as it became the most commonly affecting disease. The feature selection has played a key role in contributing the significant parameters for the disease prediction. The Binarized Whale optimizer and Binarized Grey Wolf optimizers are used for the feature selection. The support vector machine contributed well when it is tuned using the grid search technique. The promising results are achieved when the SVM is used along with Binarized Grey Wolf optimization Algorithm. The system could be implemented in realtime applications just by incorporating Glucose sensor and other relevant sensor or kits and then the prediction can be done. In future, the system can be extended for real time monitoring where sensors for reading the input values could be integrated in the system. Then it could be sent over network through GSM module or ESP8266 Module.

## References

1. http://biostat.mc.vanderbilt.edu/wiki/Main/DataSets

2. Kaveeshwar, S.A., Cornwall, J.: The current state of diabetes mellitus in India. Australas Med. J. **7**(1), 45–48 (2014). https://doi.org/10.4066/AMJ.2013.1979.PMID:24567766; PMCID:PMC3920109

3. https://www.idf.org

4. Nilashi, M., Ibrahim, O., Dalvi, M., Ahmadi, H., Shahmoradi, L.: Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Inf. Eng. **9**(3), 345–357 (2017). https://doi.org/10.1016/j.fiae.2017.09.006

5. Massaro, A., Maritati, V., Giannone, D., Convertini, D., Galiano, A.: LSTM DSS automatism and dataset optimization for diabetes prediction. Appl. Sci. **9**, 3532 (2019). https://doi.org/10.3390/app9173532

6. Fazakis, N., Kocsis, O., Dritsas, E., Alexiou, S., Fakotakis, N., Moustakas, K.: Machine learning tools for long-term type 2 diabetes risk prediction. IEEE Access **9**, 103737–103757 (2021). https://doi.org/10.1109/ACCESS.2021.3098691
7. Tiwari, P., Singh, V.: Diabetes disease prediction using significant attribute selection and classification approach. J. Phys.: Conf. Ser. **1714**(1), 012013 (2021). https://doi.org/10.1088/1742-6596/1714/1/012013

8. Annamalai, R., Nedunchelian, R.: Diabetes mellitus prediction and severity level estimation using OWDANN algorithm. Comput. Intell. Neurosci. (2021) Aug 20; 2021:5573179. https://doi.org/10.1155/2021/5573179 PMID: 34462631; PMCID: PMC8403056

9. Beghriche, T., Djerioui, M., Brik, Y., Attallah, B., Belhaouari, S.B.: An efficient prediction system for diabetes disease based on deep neural network. Complexity **2021** (2021). https://doi.org/10.1155/2021/6053824

10. Abdollahi, J., Nouri-Moghaddam, B.: Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction. Iran J. Comput. Sci. **5**(3), 205–220 (2022). https://doi.org/10.1007/s42044-022-00100-1

11. Olisah, C., Smith, L., Smith, M.: Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Comput. Methods Programs Biomed. **220**, 106773 (2022). https://doi.org/10.1016/j.cmpb.2022.106773

12. Khan, M.Z., Mangayarkarasi, R., Vanmathi, C., Angulakshmi, M.: Bio-Inspired PSO for improving neural based diabetes prediction system. J. ICT Stand. **10**(2), 179–200 (2022). https://doi.org/10.13052/jjcts2245-800X.1025

13. Saxena, R., Sharma, S.K., Gupta, M., Sampada, G.C.: A novel approach for feature selection and classification of diabetes mellitus: machine learning methods. Comput. Intell. Neurosci. **15**(2022), 3820360 (2022). https://doi.org/10.1155/2022/3820360. PMID:35463255; PMCID: PMC9033325

14. Khan, F.A., Zeb, K., Al-Rakhami, M., Derhab, A., Bukhari, S.A.C.: Detection and prediction of diabetes using data mining: a comprehensive review. IEEE Access **9**, 43711–43735 (2021). https://doi.org/10.1109/ACCESS.2021.3059343