Survey on Mining Diabetes Data and Its Applications on Diagnosing Methods in Disease Management Using Big Data

2023

Preprint

Traditional diagnostic techniques are prone to human error and time consuming. Computer-aided diagnostic techniques improve the performance and reduce the expenses. This paper presents machine learning based classifiers to detect diabetes in India and Indian Demographic & Health Survey (2019–21) dataset is considered for the analysis. Classifiers like Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and Random Forest are considered. Principal Component Analysis is used for feature reduction. Hyperparameters are tuned to achieve good performance of the classifiers. The four phases adopted in the work include preprocessing, feature extraction, classification, and analysis. From the results, the Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve in comparison with other models. The work finds application in healthcare for predictive analysis of diabetes.

“…Such challenges are due to dearth of scalability of the classifier models, longer time to construct them and problem dimensionality adversities [3].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

2023

Preprint

“…The use of multi-domain applications in a big data environment in the modern days has given more promising results. Because there is now such a vast amount of diverse and complex data, it is di cult for some classi cation techniques to utilize and create prognostic classi ers [5]. It is not easy to categorize the information at the same time due to the variety of values of the data pieces.…”

Section: Introductionmentioning

confidence: 99%

“…3) Extreme gradient boosting: The speed and effectiveness of the computation are increased through the Extreme gradient boosting (XGBoost) model. Expression ($) presents the tree ensemble method that utilizes k-additive functions to predict the output for a dataset with n samples and m features, D = {(p_(i,) q_i )}, (|D| = n, pi ∈R^m,q_i∈R as given in expression (5). It simultaneously builds decision trees and uses distributed computing techniques to evaluate big and complicated models.…”

mentioning

confidence: 99%

Boosting-based machine learning approaches for diabetes prediction using Indian demographic and health survey-2021 data

Bharamagoudar

Kallur

2023

Preprint

This study examines the effectiveness of boosting-based machine learning classifiers in identifying diabetes in the Indian population. Traditional diagnostic methods for diabetes are time-consuming and prone to errors, and computer-aided diagnostic techniques can improve performance and reduce costs. The study analyzes data from the Indian Demographic and Health Survey 2021, focusing on women aged 19–49 who tested positive for diabetes. The dataset includes clinical, anthropometric, and biochemical components, and 12,103 positive responders was considered. The study adopts predictive exploration-based boosting machine learning models, including adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Feature extraction is performed using kernel principal component analysis. The extreme gradient boosting model performs well on the dataset, with accuracy, f1-score, precision, and recall values of 81% and 83%, 81% and 82%, 81% and 88%, and 81% and 76%, respectively, before and after applying kernel principal component analysis. Adaptive boosting performs poorly, while categorical, gradient, and light gradient boosting models perform moderately. The study achieves maximum accuracy, precision, the area under the curve, and recall.

“…The diversification of data values of the features makes classifying the information challenging. Such challenges are due to the need for more scalability of the classifier models, longer time to construct them, and problem dimensionality adversities [3].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

2023

Preprint

Traditional diagnostic techniques are prone to human error and are time-consuming. Computer-aided diagnostic procedures improve performance and reduce expenses. This paper presents machine learning-based classifiers to detect diabetes in India, and Indian Demographic & Health Survey (2019–21) dataset is considered for the analysis. Classifiers like Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and Random Forest are considered. The four phases adopted in work include preprocessing, feature extraction, classification, and analysis. Principal Component Analysis is used for feature reduction. Hyper-tuning parameters are tuned to achieve good performance of the classifiers. From the results, Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve compared with other models. The work finds application in healthcare for the predictive analysis of diabetes.