Diabetes Pedigree Function Research Articles

Evaluation of sequential feature selection in improving the K-nearest neighbor classifier for diabetes prediction

The K-nearest neighbor (KNN) classifier employs distance metrics to measure the distance between the test instance and the samples used in training. With smaller samples, the KNN classifier achieves higher accuracy with low computational time. However, computing the distance between the test instance and all training samples to determine the class of the test instance requires higher computational time for a high-dimensional dataset. This research employs sequential feature selection (SFS) to select the optimal feature for diabetes prediction while reducing the computational time complexity of the KNN classifier. The KNN classifier showed effectiveness with an accuracy rate of 84.41% with nine features. The performance of the KNN improves by 2.6% when trained on the optimal features selected with the SFS. The result revealed glucose level, blood pressure (BP), skin thickness (ST), diabetes pedigree function (DPF), age, and body mass index (BMI) as the most representative features in diabetes prediction. The KNN classifier gives higher accuracy with these features. However, insulin and the number of times a woman is pregnant do not show a significant effect on the KNN classifier.

Assessing risk factors for diabetes in women: A logistic regression analysis

The prevalence of diabetes has become an increasingly pressing health challenge, with women emerging as a particularly affected demographic. As global studies underscore, the incidence of diabetes has been growing at an alarming rate, leading to substantial health consequences and creating economic burdens for healthcare systems. To address this, the research turned to an in-depth analysis using logistic regression. The data, sourced from the National Institute of Diabetes and Digestive and Kidney Diseases, encompasses findings from 769 individuals updated as of 2022. This comprehensive dataset incorporates diverse variables, from Pregnancies and Glucose levels to more specific markers like the Diabetes Pedigree Function. Intriguingly, the analysis revealed a dominant age group between 20 and 40 in the dataset. In terms of correlations, variables such as Glucose, BMI, Age, and Pregnancies displayed strong positive associations with the presence of diabetes. These findings not only corroborate existing medical knowledge but also shed light on potential risk determinants previously underemphasized. However, its essential to approach these findings with caution, acknowledging the limitations inherent in the datasets scope. Despite these constraints, the significance of this research remains profound. By emphasizing both familiar and overlooked factors, this study paves the way for more targeted and effective early interventions, ultimately aiming to improve the prognosis and quality of life for diabetic patients.

Integrating Machine Learning for Accurate Prediction of Early Diabetes

In the current world, where diabetes is day by day becoming a very common and fatal disease, it's important that proper measures be taken in order to deal with it. As per the studies, early prediction of diabetes can lead to improved treatment to avoid further complications of the disease, and in order to do so efficiently, machine learning techniques are a great deal. In this study, various factors are taken into consideration, like blood pressure, pregnancy, glucose level, age, insulin, skin thickness, and diabetes pedigree function, which together can be useful to predict whether a person has a risk of developing diabetes or not and help society with the early diagnosis of diabetes. This model is trained using three main classification algorithms, namely support vector, random forest, and decision tree classifiers. The prediction results of each of the classifiers are summarized in this study, and the decision tree gives 78.89% accuracy.

Diabet Prediction with Logistic Regression

Diabetes is a common chronic disease that seriously challenges countries worldwide. However, there are problems suchas low detection rate, low awareness rate, low acceptance rate, and low treatment compliance rate in the diagnosis andtreatment process. This paper aims to construct a Logistic Regression Model to predict whether a patient has diabetesor not and to investigate the key factors that can be used to diagnose whether a patient has diabetes or not to promotethe development of diabetes treatment. The research results show that the patient’s characteristics, such as the numberof times prior, body mass index, and diagnostic measurements such as plasma glucose concentration 2 hours in anoral glucose tolerance test, trips skin fold thickness, 2-hour serum insulin, and diabetes pedigree function will have asignificant impact on whether the patient has diabetes. Therefore, the above six variables should be focused on whendiagnosing diabetes mellitus.

Penerapan Particle Swarm Optimization Untuk Meningkatkan Kinerja Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Diabetes

The study aims to apply the KNN algorithm to classify diabetes by combining the PSO algorithm as a selection so as to obtain the best accuracy from the KNN algorithm in classifying, so that it can be applied to diagnose diabetes. This research consists of several stages of research including dataset collection, data pre-processing, data sharing, finding the optimal k-value to the classification process and performance or accuracy testing. From this research, the accuracy of the KNN algorithm before feature selection using PSO is 75% at k-optimal 19 and after feature selection using PSO is obtained an increase in accuracy to 77.213% at the same k value with features that affect the pima dataset, namely Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, and Age. Thus the use of KNN with PSO feature selection can be used to identify diabetes because it has a much better level of accuracy.

A robust predictive diagnosis model for diabetes mellitus using Shapley-incorporated machine learning algorithms

With the rapid advancement and integration of Artificial Intelligence (AI) in medicine, the need for new developments and accuracy in Machine Learning (ML) models and algorithms has increased substantially. Meanwhile, the limited availability of medical data has hampered the rapid advancement of AI, and robust models that can leverage the available data are needed. In this paper, we propose robust frameworks for the predictive diagnosis of diabetes mellitus using the limited data generated among women aged 21 to 81. The proposed frameworks have data augmentation, analysis of the attributes, and missing data imputations in common as the preliminary steps. We used Shapley Additive Explanation (SHAP) to extract feature importance and ascertain the most important features for fitting Extra Tree (ET), Random Forest (RF), Adaboost, and Xgboost models. The SHAP shows that glucose is the particular feature that contributes most to the prediction of diabetes, while in combination with age and Body Mass Index (BMI), they have a much more impact. Additionally, BMI and diabetes pedigree function also rate high for the prediction of diabetes; so, if it is hard to manage blood glucose, the focus can be switched to the management of BMI and the diabetes pedigree function. Informed by SHAP, we use a new dataset coined from the original one to fit the ML algorithms used for the prediction of diabetes, for which Xgboost and Adaboost performed better than other models with an accuracy of 94.67% each and an F1 score of 95.27 and 95.95, respectively.

Physiological Factors Affecting Diabetes in Women using Binary Logit Regression Model

The purpose is to use statistical methods to analysis some female data, trying to figure out what physiological factors will affect the occurrence of diabetes on female and how. Method: using dataset from “AKSHAY DATTATRAY KHARE” in Kaggle, which comes from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) in original. In particular, all patients here are females of Pima Indian descent who are at least 21 years old. Use binary logit regression model to analysis. Result: Pregnancies, skin thickness, insulin and blood pressure, as long as age can’t be taken into account (p>0.05). The remaining three elements, which is glucose (OR=1.039), BMI(OR=1.073), diabetes pedigree function (OR=3.130), are all risk factors. The overall correct rate is 78.3%. The conclusion is using binary logit regression model can analysis factors related diabetes well. The logistic regression model among the influencing factors of diabetes shows that glucose, BMI, and diabetes pedigree function are likely to cause diabetes, and diabetes pedigree function has a greater impact on the psychological function of patients.

Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN

Diabetes is a chronic, non-communicable disease, and a long-term health condition that affects how the body uses glucose, the type of sugar that gives energy. In Indonesia, diabetes ranks as the sixth highest cause of death, following conditions related to childbirth. In 2021, Indonesia has a total of 19.5 million diabetes patients, making it the fifth-highest in the world. Some machine learning research has used data from the PIDD (PIMA Indian Diabetes Dataset) to predict diabetes. In this research, in addition to prediction accuracy, data complexity is also important. This research analyzes important features in the PIMA Indian database using the KNN (k-nearest neighbor) method for classification. The results show that using KNN with k=22 value results in the highest accuracy of 83.12%. The analysis also found that the important features required by the KNN method to achieve high accuracy from the PIMA Indian database, in order of importance, are glucose, age, insulin, blood pressure, Body Mass Index, pregnancy, skin thickness, and diabetes pedigree function. However, when used in the KNN classification method, the diabetes pedigree function feature was found to be unnecessary, not relevant, and can be reduced.

Predicting the Onset of Diabetes with Machine Learning Methods.

The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018-2020 and 2021-2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models.

Explanatory Factors of Gestational Diabetes Pedigree Function of Pima Indian Heritage Women

Diabetes pedigree function (DPF) predicts diabetes risk based on age and family history, regardless of whether the person is diabetic or not.

Evaluating the Risk of Type 2 Diabetes Mellitus Using Artificial Neural Network

To identify risk factors, neural network analysis is used to create disease prediction models, including diabetes. The goals of this study were to identify diabetes risk factors and determine their relative contribution using artificial intelligence as a mode of prediction. The current investigation was led by breaking down the dataset, as shown below. We chose a dataset from Kaggle. The diabetes dataset was from India. It has 763 female members, 497 of whom have no diabetes and 266 who have type 2 diabetes. We used neural network analysis to create mathematical models and visualize the distribution of diabetic risk factors. The significance level was set at 0.05. The current study found that the following risk factors were ranked in order of importance: Diabetes Pedigree Function, age, glucose, skin thickness, blood pressure, BMI, insulin, and number of pregnancies. When combined, neural network analysis is effective in developing mathematical models that can predict disease risk factors.

Prediction of the Probability of Having Type 2 Diabetes by Using Logistic Regression

Diabetes has been the primary cause of disability and mortality on a global scale. It is crucial to take action to reduce the rising incidence of diabetes. The treatment and control of diabetes are aided by early diagnosis of diabetics and pre-diabetics. A deeper comprehension of the diabetes risk factors is necessary to perform the early screening of diabetics more effectively. In order to determine the type 2 diabetes risk variables and forecast the likelihood that Pima Indian women will develop the disease, logistic regression is utilized in this essay. Pregnancy, glucose, diabetes pedigree function and body mass index(BMI) are four variables in the model that have been identified to be significant predictors. All relevant predictors had positive relationships with the result, according to the analysis. The cross-validated test error rate for the model is 15.74%, and its accuracy is 76.28%. In conclusion, it is suggested that it is conceivable to forecast type 2 diabetes using the data currently available, and the model found suggests that lowering the rate of pregnancies, blood glucose levels, and body mass index may be a way to minimize the prevalence of diabetes.

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.

Heteroscedasticity Detection in Cross-Sectional Diabetes Pedigree Function: A Comparison of Breusch-Pagan-Godfrey, Harvey and Glejser Tests

Diabetes is a serious defect that does not make the body to have enough insulin, and thereby allowing blood sugar to stay in the bloodstream more than the body requires and over time causes serious problems relating to health. So, predicting if a person has diabetes or not using the linear model surface, but a major challenge arises if there is heteroscedasticity in the model, which can make the least square estimates inefficient. So, there is a need to know the method that is best for detecting heteroscedasticity so as not to rely on inefficient model for predicting diabetes. This research therefore aimed at comparing the Breusch-Pagan-Godfrey (BPG), Harvey and Glejser tests for detecting heteroscedasticity in cross-sectional data. To achieve this, data were collected on Diabetes Pedigree Function (DPF), Plasma glucose concentration a 2 hours in an oral glucose tolerance test (G), 2-Hour serum insulin (mu U/ml) (I), and Triceps skin fold thickness (mm) (S) from National Institute of Diabetes and Digestive and Kidney Diseases (1990) comprising 768 observations. The data was divided into two, small sample and large sample. The result of the regression analysis showed that skin fold thickness is the most important factor that can predict diabetes in a patient, followed by plasma glucose concentration, and then by insulin. The result for heteroscedasticity showed that, heteroscedasticity is not present in small dataset using the three tests. However, for the large sample, both the Breusch-Pagan-Godfrey and Glejser detect heteroscedasticity, but Harvey did not. Hence, it is advisable to use either Breusch-pagan or Glejser tests because they are more sensitive to heteroscedasticity in diabetes patient data.

Analysis on Diabetes Prediction Using Different Classifiers Techniques

Huge amount of data is fetched from various resources. Data that is required from large data repository can be extracted with the use of data mining techniques, as there might be relevant and irrelevant data present in the repository. Thus minimizes the access of data from repository, so for increasing the data usage ability, data mining techniques are required. In data mining data is classified in different classes which are then used to implement a various disease prediction model. Similarly diabetes can be predicted using these prediction models based on different parameters. Parameters include Pregnancies, Blood Pressure, Skin Thickness, Glucose, Insulin, Diabetes Pedigree Function, Age and many more various classifiers. Different classifiers like CART, RF, SVM, LDA, KNN are being applied on PIMA dataset for the data at the confidence level of 0.95. CART has highest accuracy of 88%. Lowest accuracy is of KNN. So prediction using CART based classifiers is best.

Pregnancy’s Relationship With Diabetes and Induced Hypertension: A Mini-Review

It is pointed out in many articles that induced hypertension and gestational diabetes are well linked to pregnancy, but this relationship is not well determined. The report focuses on the pregnancy’s relationship with diabetes and induced hypertension. It is found herein that mean pregnancy is directly related with glucose levels (GLUC) (P=0.07), age (P<0.01), BMI (P=0.07), Diabetic Women (DW) status (P=0.01), interaction effect of Triceps SkinFold Thickness (TSFT) and age (TSFT × Age) (P<0.01), while it is inversely related with GLUC × Age (P=0.02), TSFT × BMI (P<0.01), Insulin (INSU) (P=0.06) and Diabetes Pedigree Function (DPDF) (P=0.03). Variance of pregnancy is directly related with GLUC (P=0.07), Diastolic Blood Pressure (DBP) (P=0.04), GLUC × TSFT (P<0.01), DBP × TSFT (P<0.01), DPDF (P=0.01), INSU × BMI (P=0.15), while it is inversely related with GLUC × DBP (P=0.01), TSFT (P<0.01), TSFT × DPDF (P=0.03), INSU (P=0.07). It is easily interpreted that pregnancy’s mean is well connected to diabetic parameters such as INSU and GLUC levels, history of DW, DPDF, BMI, GLUC × Age, while its variance is connected to diabetic and hypertension parameters such as GLUC, DPDF, DBP, GLUC × TSFT, DBP × TSFT, INSU × BMI, GLUC × DBP, TSFT × DPDF, INSU. Moreover, it is derived that a diabetic female has a higher chance to be pregnant than a normal woman.

COMPARISON OF MACHINE LEARNING METHODS FOR A DIABETES PREDICTION INFORMATION SYSTEM

Diabetesisadiseaseforwhichthereisnopermanentcure;therefore,methodsandinformationsystemsarerequired for its early detection. This paper proposes an information system for predicting diabetes based on the use of data mining methods and machine learning algorithms. The paper discusses a number of machine learning methods such as random forest, AdaBoost algorithm, multilayer perceptron, neuro–like structure of Consecutive Geometric Transformations Models (CGTM), linear regression based on the stochastic gradient descent, generalized regression neural network and regression based on the support vector machine. The Pima Indian Diabetes dataset collected from the UCI machine learning repository was used in the research. The dataset contains information about 768 patients and their corresponding nine unique attributes: the number of times of pregnancy; plasma glucose concentration for two hours in an oral glucose tolerance test; diastolic blood pressure; the thickness of the folds of the skin of the triceps; the concentration of serum insulin for two hours; body mass index; a function of diabetes heredity; the age of a person; the result of a variable class (0 – no diabetes, 1 – a sick person). The research has been carried out to improve the prediction index based on the Recursive Feature Elimination method. It was found that the logistic regression model performed well in predicting diabetes. It has been shown that in order to use the created model to predict the likelihood of diabetes mellitus with an accuracy of 78%, it is necessary and sufficient to use such indicators of the patient's health status as the number of times of pregnancy, the concentration of glucose in the blood plasma during the oral glucose tolerance test, the BMI index and the result of the calculation of the heredity functions "Diabetes Pedigree Function".

Predict Diabetes Mellitus Using Machine Learning Algorithms

Diabetes mellitus has become a very frequent disease that affects totally different organs of human body. Diabetes cause diverge depending on genetic, family history, health and environmental factors. Diabetes mellitus refers to a gaggle of diseases that affect how your body uses blood glucose. The underlying reason behind diabetes varies by type. But, despite what kind of diabetes you’ve got, it will cause excess sugar in your blood. Diabetes will be of two types, they are Type1 Diabetes and Type2 Diabetes. Early prediction will help in society a lot. It will provides the humanlife in safe way. The aim of this analysis is to develop a system that predicts the diabetes with a better accuracy. Parameters used to predict the type of Diabetes Mellitus are Glucose, Pregnancies, skin thickness, Blood pressure, Insulin, BMI, Diabetes pedigree function, age and upshot. In this we are with different machine learning algorithms, namely SVM, ANN, Decision tree, Logistic regression and Farthest first to predict the accuracy. Our experimental results show that farthest first attain superior correctness compare to dissimilar machine learning techniques.

Effects of Insulin on Gestational Pima Indian Heritage Females

Diabetes and obesity reach epidemic proportions all over the world, while the effects of insulin resistance and its consequences are gaining prominence. The article derives the effects of insulin on gestational Pima Indian heritage females with minimum 21 years old. It is identified herein that mean insulin level for gestational women (GW) is positively linked with glucose level (P=0.0055), while it is negatively linked with age (P=0.0095). Mean insulin level for GW is independent of pregnancies (P=0.1866) and diabetes pedigree function (DPF) (P=0.5321), while it is partially positively linked with their joint interaction effect Pregnancies*DPF (P=0.0864). It is positively linked with blood pressure (BP) (P<0.0001) and triceps skin-fold thickness (TST) (P<0.0001), while it is negatively linked with the joint interaction effects BP*TST (P<0.0001) and DPF*TST (P<0.0001). In addition, mean insulin level is negatively linked with body mass index (BMI) (P=0.0001), while it is positively partially linked with the interaction effect BMI*DPF (P=0.1312). Variance of insulin level is positively linked with pregnancies (P=0.0184) and age (P=0.0027), while it is negatively linked with their joint interaction effect Pregnancies*Age (P=0.0418). Also variance of insulin level is partially negatively linked with BMI (P=0.0738) and glucose level (P=0.1439), while it is partially positively linked with their joint interaction effect BMI*Glucose (P=0.1472). Further, variance of insulin level is negatively linked with DPF (P=0.0309). It is concluded that for GW, insulin level increases with the increase of glucose level, BP, TST, and the interaction effects Pregnancies*DPF & BMI*DPF, and it decreases with the increase age, BMI, and the interaction effects BP*TST & DPF*TST. GW should be careful on her glucose level, BMI, BP & TST.

Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.