Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods

Thotad, Puneeth N; Bharamagoudar, Geeta R.; Anami, Basavaraj S.

doi:10.1016/j.dsx.2022.102690

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…[16] used tree diagrams and were able to compare the needs for primary care (PHC), and advanced care (AHC), to determine cancer care coverage rates in Indonesia. [19] used decision tree as one of the models to investigate diabetes mellitus disease, which is considered as a major health issue in India. [4] used machine learning models like the decision tree, neural network, random forest, support vector machines, and others to predict health conditions, which are applied by the US Health and Retirement Study data.…”

Section: Research Backgroundmentioning

confidence: 99%

Health Insurance Premium Pricing Using Machine Learning Methods

Ahmad Nur Azam Ahmad Ridzuan,

Aina Zafirah Azman,

Fatin Alya Marzuki

et al. 2024

ARASET

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Health insurance is important alongside life insurance products. This product’s subscription is gradually rising and has become one of the public’s main considerations due to their awareness on medical and surgical costs. This study is aimed at (i) identifying whether the independent factors are important in predicting the dependent variable, and (ii) to determine which model (logistic regression model/decision trees/neural networks) is the best model to be utilised. The SAS Enterprise Miner (E-Miner) was used to analyse the data and to select the best model. At the end of this study, the measurements like the Maximum Absolute Error (MAE), Average Squared Error (ASE), Root Average Squared Error (RASE), and Sum Squared Error (SSE) in the decision tree model indicated the lowest errors and led to the selection of the decision tree as the best model to be used in health insurance premium pricing.

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Section: Research Backgroundmentioning

confidence: 99%

Health Insurance Premium Pricing Using Machine Learning Methods

Ahmad Nur Azam Ahmad Ridzuan,

Aina Zafirah Azman,

Fatin Alya Marzuki

et al. 2024

ARASET

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“…The second category includes interpretable models characterized by explicit prediction models. Most of these models rely on decision trees [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. Although the methods based on these trees [ 70 ] could provide explicit knowledge, in many cases, it is challenging to linearize the resulting acyclic decision graphs into simple decision rules.…”

Section: State Of the Artmentioning

confidence: 99%

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

Falco

Cioppa

Koutný

et al. 2023

Sensors

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In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

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“…The data of the Indian demographic and health survey: 2019-2021 (IDHS-21) from the DHS program analyzed and provided accurate and typical data on population, health, and nutrition. Anthropometrical, biological, and healthcare data on women were considered as part of the health survey [3]. 636,699 families, 101,839 males, and 724,115 women are taken in the poll.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

“…Data preparation shows a signi cant part in data science methods. The few stages in the preprocessing of data are cleaning, changing to other forms, and decreasing information, as discussed in [3]. The unprocessed information changes in a way that a model can be able to collaborate with it.…”

Section: B Data Preprocessingmentioning

confidence: 99%

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

Thotad

Bharamagoudar

Kallur

2023

Preprint

Self Cite

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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.

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Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods

Cited by 16 publications

References 10 publications

Health Insurance Premium Pricing Using Machine Learning Methods

Health Insurance Premium Pricing Using Machine Learning Methods

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

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

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