=Paper=
{{Paper
|id=Vol-3734/invited4
|storemode=property
|title=Research on the application of Machine Learning in
predicting diabetes
|pdfUrl=https://ceur-ws.org/Vol-3734/paper4.pdf
|volume=Vol-3734
|authors=Wenfeng Ye,Hui Zeng
|dblpUrl=https://dblp.org/rec/conf/iccic/YeZ24
}}
==Research on the application of Machine Learning in
predicting diabetes==
https://ceur-ws.org/Vol-3734/paper4.pdf
Research on the application of Machine Learning in
predicting diabetes
Wenfeng Ye 1 and Hui Zeng2, β
1 School of Computer Science, University of Nottingham Malaysia, Kuala Lumpur Malaysia.
2 School of Midwifery, Ganan Medical University, Ganzhou, China.
Abstract
This article aims to establish a predictive model for diabetes occurrence using machine learning
methods. We utilized a clinical dataset from patients at Sylhet Diabetes Hospital in Sylhet,
Bangladesh, including various clinical features related to diabetes such as Age, Gender, Polyuria,
Polydipsia, sudden weight loss, weakness, Polyphagia, Genital thrush, visual blurring, Itching,
Irritability, delayed healing, partial paresis, muscle stiffness, Alopecia, and Obesity. We
performed data preprocessing and feature engineering, converting categorical variables into
numerical form and standardizing the data. Subsequently, we experimented with various
machine learning algorithms including logistic regression, decision trees, and support vector
machines. Through cross-validation and grid search for parameter optimization, we selected
multiple linear regression as the final predictive model.
We evaluated the model's performance on the test set using metrics such as mean squared error
(MSE), mean absolute error (MAE), R-squared (R^2), and root mean squared error (RMSE).
Experimental results indicate that our model demonstrates high accuracy and reliability in
predicting diabetes occurrence. Through this model, we can promptly identify individuals at risk
of diabetes, providing doctors with more accurate diagnostic and treatment recommendations,
and potentially offering crucial decision support for diabetes prevention and management.
Keywords
Machine learning; Linear regression; Polynomial regression
1. Introduction
Diabetes is a common and serious chronic disease that significantly impacts the quality of
life and health of millions of people worldwide [1]. According to data from the World Health
Organization (WHO), diabetes has become a global epidemic, with an estimated 320 million
people expected to be affected globally by 2030. Diabetes not only imposes physical health
burdens on patients but also leads to various complications such as cardiovascular diseases,
kidney diseases, and retinopathy, posing a serious threat to patients' lives [2].
ICCIC 2024: International Conference on Computer and Intelligent Control, June 29β30, 2024, Kuala Lumpur,
Malaysia
* Corresponding Author
hfywy4@nottingham.edu.my (W. Ye); 2904941810@qq.com (H. Zeng)
0009-0004-4497-3418 (W. Ye); 0009-0007-6117-3596 (H. Zeng)
Β© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Early prevention and diagnosis are particularly crucial for the prevention and control of
diabetes. However, due to the complex pathogenesis of diabetes and the lack of obvious
early symptoms, many patients are not aware of their health [3].
problems in the early stages of the disease, leading to missing the optimal treatment
window. Therefore, establishing effective diabetes prediction models to timely identify
individuals at higher risk of the disease is of great significance for reducing the incidence of
diabetes and improving patients' quality of life.
Traditional diabetes prediction methods mainly rely on doctors' experience and clinical
indicators such as blood glucose levels and insulin sensitivity. However, these methods have
drawbacks including long diagnosis times, high costs, and reliance on medical resources.
With the continuous development of machine learning and artificial intelligence
technologies, it has become possible to construct diabetes prediction models using big data
and deep learning methods. Machine learning algorithms can learn from massive clinical
data to discover potential patterns and features of diabetes occurrence, providing doctors
with more accurate and rapid diabetes diagnosis and prediction services, thereby
contributing to improving diabetes prevention and control efforts [4].
Therefore, this study aims to utilize machine learning methods, based on clinical data, to
construct a diabetes prediction model, achieving fast and accurate prediction of diabetes
occurrence. This will provide medical institutions and patients with more effective
prevention and management strategies, ultimately reducing the incidence of diabetes and
improving public health. Through the conduct of this research, it is hoped to provide
important theoretical and practical support for further research and application in the field
of diabetes prediction [5].
The dataset utilized in this study comprises 520 samples. After data cleansing, anomalies
and missing values were addressed, and features in the dataset were processed, including
transformation, encoding, and standardization. Additionally, we explored the distribution
of age data by plotting the histogram of the target variable. The histogram revealed a normal
distribution of age data.
Figure 1: Distribution histogram of age.
For categorical variables, we employed one-hot encoding for transformation. For
numerical variables, standardization was conducted to ensure uniform scales across
different features [6].
In response to the issue of excessive or redundant features, feature selection was
performed to identify features with significant impact on the target variable. As a result, 16
features were retained, as depicted in the following figure. These features appear to exhibit
strong multicollinearity among them.
For categorical variables, we employed one-hot encoding for transformation. For
numerical variables, standardization was conducted to ensure uniform scales across
different features.
In response to the issue of excessive or redundant features, feature selection was
performed to identify features with significant impact on the target variable. As a result, 16
features were retained, as depicted in the following figure. These features appear to exhibit
strong multicollinearity among them.
Figure 2: Heavy correlations between features.
In this study, we adopted the mean square error (MSE) as the index to evaluate the
prediction performance of the model. The mean squared error is a measure of the squared
difference between the predicted value of the model and the actual observed values. It is
calculated by summing the squared differences between the predicted value and the actual
observed value for each sample and then dividing by the number of samples. A smaller MSE
indicates a more accurate model prediction [7].
ππ
1 2
ππππππ = οΏ½οΏ½π¦π¦ ππ β π¦π¦οΏ½οΏ½ (1)
ππ
ππ=1
2. Model Establishment
2.1 Normal equations in linear regression
Linear regression is a statistical method used to establish a relationship between an
independent variable (or feature) and a dependent variable. It assumes that there is a linear
relationship between the independent variable and the dependent variable, that is, it can be
described by a straight line. The goal of linear regression is to find the best-fit line that
minimizes the error between the predicted value and the observed value.
Normal equations are a method used to solve the parameters of linear regression models.
It finds the best fitting line by minimizing the sum of squared residuals.
The linear regression model is of the form.
π¦π¦ = Ξ²0 + Ξ²1 π₯π₯1 + Ξ²2 π₯π₯2 + β― + Ξ²ππ π₯π₯ππ + (2)
By taking the partial derivatives of the model parameters and setting them equal to zero,
we can obtain the normal equation.
Ξ² = (ππ ππ ππ)β1 ππ ππ π¦π¦ (3)
By taking the partial derivative of the loss function with respect to the parameter vector
π½π½, setting it equal to zero, and then solving for the parameter vector π½π½, we can obtain the
normal form of the above equation.
We fit a linear regression model to the training data in our study and evaluate the model
performance on the test set. The accuracy and generalization ability of the model were
evaluated by calculating the coefficients and intercepts of the model, as well as using the
mean squared error [8].
Below is the confusion matrix for the mean variance of the linear regression equation
model
Figure 3: Confusion matrix for the mean variance of the positive regression equation model.
2.2 Linear Regression: Gradient Descent
Stochastic Gradient Descent (SGD) is an optimization algorithm used to train machine
learning models, especially on large-scale datasets. It is an iterative optimization algorithm
in which the objective function of each round calculation is no longer the whole sample
error, but only a single sample error, that is, only one sample at a time is substituted to
calculate the gradient of the objective function to update the weight, and then the next
sample is repeated until the loss function value stops decreasing or the loss function value
is less than a tolerable threshold. Its equation can be described as
Stochastic Gradient Descent (SGD) is an optimization algorithm used to train machine
learning models, especially on large-scale datasets. It is an iterative optimization algorithm
in which the objective function of each round calculation is no longer the whole sample
error, but only a single sample error, that is, only one sample at a time is substituted to
calculate the gradient of the objective function to update the weight, and then the next
sample is repeated until the loss function value stops decreasing or the loss function value
is less than a tolerable threshold. Its equation can be described as
ΞΈ = ΞΈ β Ξ±βπ½π½οΏ½ΞΈ; π₯π₯ (ππ) , π¦π¦ (ππ) οΏ½ (4)
After the model is trained, the coefficients and bias terms of the model are obtained, and
the test set is used to make predictions. Then, the mean square error (MSE) was used as the
evaluation index to calculate the difference between the model prediction results and the
true value.
Figure 4: Mean error comparison between the SGD model and the general linear mode.
The final evaluation results show that the model has a small mean square error,
indicating that it performs well in predicting the target variable. Compared to the previous
model using the normal equation method, this model based on stochastic gradient descent
is more accurate and generalizable, better able to adapt to changes in the data and provide
more reliable predictions
2.3 Ridge Regression Models in L2 Regularization
Ridge regression is an extended form of linear regression. It introduces an L2 norm penalty
term to constrain the complexity of the model, so as to avoid the overfitting problem. In
ridge regression, the goal is to minimize the sum of a loss function and an L2-norm penalty
term, where the loss function is usually the sum of squared residuals (RSS) [9].
2 2
minοΏ½|π¦π¦ β ππΞ²|οΏ½ + Ξ±οΏ½|Ξ²|οΏ½2 (5)
Ξ² 2
Our goal is to minimize a loss function that consists of a squared loss term and an L2
regularization term of the following form.
ππ ππ
1
π½π½(π½π½) = οΏ½(βπ½π½ (π±π± π’π’ ) β π¦π¦ππ )2 + ππ οΏ½ ππππ2 (6)
2ππ
ππ=1 ππ=1
The analytical solution of ridge regression can be obtained by least squares method. By
taking the derivative of the objective function and setting the derivative to zero, an
analytical expression for the regression coefficients can be obtained.
π½π½ = (ππ ππ ππ + ππππ)β1 ππ ππ π¦π¦ (7)
Ridge regression can effectively reduce the amplitude of regression coefficients by
introducing L2 regularization term, thereby reducing the complexity of the model and
avoiding overfitting. In addition, ridge regression can also deal with multicollinearity
problems and improve the stability and generalization ability of the model [10].
2.4 Lasso (Least Absolute Shrinkage and Selection Operator) mode.
Lasso model is a regularization method based on linear regression. Its core idea is to
minimize the loss function while adding the L1 norm penalty term, so that the model
parameters tend to be sparse, and the coefficients of some features are compressed to zero
to realize feature selection and model simplification.
The objective is to minimize a loss function, which consists of a squared loss term and an
L1 regularization term of the following form.
m n
1
J(ππ) = οΏ½(hππ (π±π± π’π’ ) β yi )2 + Ξ» οΏ½οΏ½ΞΈj οΏ½ (8)
2m
i=1 j=1
An efficient solution to Lasso is coordinate descent. The method updates only one
coefficient at each step and treats the other coefficients as constants. Specifically, for each
coefficient we fix it in turn, then minimize the objective function, and update this process
iteratively until convergence.
m n
1
ΞΈj = argminΞΈ οΏ½ οΏ½(hΞΈ (xi ) β yi )2 + Ξ» οΏ½οΏ½ΞΈj οΏ½οΏ½ (9)
j 2m
i=1 j=1
An important property of Lasso Regression is that it tends to eliminate unimportant
weights [11].
For example: for relatively large values of Ξ±, higher-order polynomials degenerate to
quadratic or even linear: higher-order polynomial features. The weight of is set to 0.
That is, Lasso Regression can automatically perform feature selection and output a
sparse model (only A few features have nonzero weights)
Sub gradient vectors for Lasso Regression
1 2
π½π½(Ξ²) = οΏ½|π¦π¦ β ππΞ²|οΏ½2 + Ξ±οΏ½|Ξ²|οΏ½1 (10)
2ππ
ROC curve is a common tool used to evaluate the performance of classification models. It
shows the relationship between True Positive Rate and False Positive Rate.
Figure 5: About the roc curve of this model.
Based on these results, we can conclude that ridge regression model and Lasso
regression model are better choices for predicting the occurrence of diabetes, they can
explain the data more accurately and have smaller prediction error
The Ridge Regression and Lasso Regression models perform well on this dataset, with
low mean square error, mean absolute error, and root mean square error, and r-squared
values close to 1, indicating a good fit to the data. In contrast, the Normal Equation and
Stochastic Gradient Descent models perform poorly, with negative values of RΒ² indicating
that the model fails to fit the data well [12].
Here is an overview of the training of the four models.
3. Solutions And Results
3.1 The Solution of Trending model
1. Comparison between normal equation model and Stochastic Gradient Descent (SGD)
model:
The normal equation model and the SGD model have the same performance metrics,
which indicates that they produce similar prediction results [13].
However, these models have negative R-squared values (-0.479319), indicating that
their performance is below the horizontal line that passes through the mean of the data.
The mean square error (MSE) and root mean square error (RMSE) are relatively high,
indicating a large difference between the predicted and actual values.
2. Ridge regression model and Lasso regression model
The ridge regression and Lasso regression models significantly outperform the normal
equation and SGD models in terms of performance.
The R-squared values for these two models (0.651793) indicate that they explain about
65%, indicating that the models fit the data very well.
The mean square error (MSE) and root mean square error (RMSE) are relatively low,
indicating that the difference between the predicted and actual values is small compared to
the normal equation and the SGD model.
Furthermore, the two models have relatively low mean absolute error (MAE), indicating
their high accuracy in predicting the target variable [14].
Figure 6: Four evaluation metrics for the four regression models.
In general, indicators of the models in the experiment, ridge regression and Lasso
regression models are better than the normal equation and SGD models in terms of
prediction accuracy and fitting
Figure 7: training of the four models.
3.2 Model tuning as well as supplemental experiments
(1) In this experiment, we explored one of the most common regularization techniques used
in linear regression models, Lasso regression introduces the L1 term into the loss function
to make the model parameters sparse, so as to achieve the effect of feature selection and
dimensionality reduction. However, in practice, the performance of Lasso regression is
affected by the regularization parameter alpha. Therefore, this experiment aims to further
optimize the performance of the Lasso regression model by tuning the regularization
parameter alpha [15].
Firstly, we selected a representative dataset and performed data preprocessing and
preparation. We then built a basic Lasso regression model and used grid search with k-fold
cross-validation to find the best regularization parameter over a range of predefined alpha
values. This process aims to minimize the fitting error of the model on the training data
while maintaining the ability to generalize to new data.
After the parameter tuning was completed, we retrained the Lasso regression model and
evaluated the performance on an independent test set. We evaluated the prediction
accuracy, generalization ability, and robustness of the model and quantified the
performance of the model using metrics such as mean square error, mean absolute error,
R-squared score, and root mean square error.
Through the analysis of the Test results, we conclude that the model is better than that
of the basic model. It shows that by adjusting the regularization parameter appropriately,
the fitting effect and generalization ability of Lasso regression model can be improved, and
its application value in practical problems can be improved. In addition, we discuss the
limitations of the experimental results and future research directions in order to further
refine and advance the research in this area. mean square error, mean absolute error, R-
squared score, and root mean square error [16].
(2) Feature selection is a key step in machine learning and statistical modeling. It aims
to pick out the most predictive features from the original feature set to improve the
performance and generalization ability of the model. Feature selection can help reduce the
dimensionality of the data, reduce the complexity of the model, improve the interpretability
of the model, and speed up the model training process. In practice, feature selection is one
of the key steps in building efficient and reliable machine learning models.
Lasso regression is a commonly used linear regression method, which has the ability of
automatic feature selection. By adding an L1 regularization term to the objective function,
Lasso regression can compress the coefficients of some features to zero, thus achieving
feature sparsity. This allows Lasso regression to perform well on datasets with a large
number of features and to discover the most relevant features.
The purpose of this experiment is to explore the effect of different feature selection
methods on the performance of Lasso regression models. We will compare three commonly
used feature selection methods: Feature selection is a key step in machine learning and
statistical modeling. Its purpose is to select the most predictive features from the original
feature set to improve the performance and generalization of the model. Feature selection
helps to reduce the dimensionality of the data, reduce the complexity of the model, improve
the interpretability of the model, and speed up the training process of the model. In practice,
feature selection is one of the key steps in building efficient and reliable machine learning
models.
Lasso regression is a commonly used linear regression method, which has the ability of
automatic feature selection. By adding an L1 regularization term to the objective function,
Lasso regression can compress the coefficients of some features to zero, thus achieving
feature sparsity. This allows Lasso regression to perform well on datasets with a large
number of features and to discover the most relevant features.
The purpose of this experiment is to explore the effect of different feature selection
methods on the performance of Lasso regression model. We will compare three commonly
used feature selection methods: Wrapper method, Filter method and Embedded method,
and analyze their effect in Lasso regression model
Before performing feature selection, we first trained a Lasso regression model on the
original feature set as a baseline model. We recorded the performance metrics of the
baseline model, including mean squared error (MSE), Mean absolute error (MAE), R-
squared (R-Β²), and others [17].
3.3 Comparison of feature selection methods
Next, we used three different feature selection methods and compared their impact on the
performance of the Lasso regression model:
Wrapper method (RFE) : Recursive Feature Elimination (RFE) method is used for feature
selection. RFE works by repeatedly training the model and gradually eliminating the least
important features until the desired number of features is reached. We chose an appropriate
number of features and trained the Lasso regression model on these features.
Filter method (correlation coefficient) : Use correlation coefficient for feature selection.
The correlation coefficient measures how linearly correlated the features are with the target
variable. We selected the features with the highest correlation with the target variable and
trained a Lasso regression model on these features.
Embedded method (L1 regularization) : A feature selection mechanism that uses L1
regularization itself. L1 regularization can compress the coefficients of some features to
zero to achieve feature sparsity. We trained a Lasso regression model and recorded the
features corresponding to nonzero coefficients.
3.4 Performance comparison
Finally, we compare the Lasso regression model performance under the three feature
selection methods. We analyze the differences in model performance metrics among the
various methods and explore the reasons behind these differences. Through the
performance comparison, we draw conclusions and recommendations regarding the
selection and application of feature selection methods [18].
4. Conclusion and Outlook
The method of feature selection using Lasso regression model in our study has shown good
results on this problem. By regularizing the penalty, the model is able to automatically select
features that have a significant impact on the prediction of diabetes, thus improving the
generalization ability and interpretability of the model [19].
Combined with the feature selection mechanism of Lasso regression, the model has
strong interpretability [20]. We can get a clear picture of which features play a key role in
predicting diabetes, which can help medical researchers to understand the pathogenesis of
the disease, and evaluate the results based on our model, We can see that the model
performs well on metrics such as mean squared Error (MSE) Mean absolute error (MAE R-
squared (R^2) root mean squared error (RMSE) [21]. This indicates that our model can
predict the occurrence of diabetes relatively accurately while maintaining high precision
and recall
ο¬ Further research
Based on the performance of the current model, we can further explore how to improve
the model and improve its predictive performance [22]. For example, we can try other
regularization methods or use more complex model architectures to improve the prediction
accuracy of our model [23].
In summary, our developed model shows good performance and high explanatory power
in predicting the occurrence of diabetes, which provides an important reference for further
medical research. We can use machine learning methods for analysis and prediction.
Through experiments, it is not difficult to find that the second-order polynomial regression
[24].
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