Deep learning approach for diabetes prediction using PIMA Indian dataset

Naz, Huma; Ahuja, Sachin

doi:10.1007/s40200-020-00520-5

Cited by 166 publications

(52 citation statements)

References 31 publications

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“…They used the classifiers Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Deep Learning (DL) and achieved 90% to 98% accuracy. They also show that the deep learning approach achieved the maximum accuracy, 98.04% [11].…”

Section: Literature Reviewmentioning

confidence: 86%

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Bandyopadhyay¹,

Bose²,

Goyel³

2021

Preprint

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Diabetics or Diabetic Mellitus is a metabolic disorder of blood sugar levels in the human body. It is a major non-communicable disease and involved many serious health risk issues. This disease is rapidly increasing in India. It is a chronic condition and it occurs when a body doesn't produce enough insulin hormone to control the blood sugar level. In this study, different variables have been analyzed that cause the diabetics, and different machine learning algorithms are used to predict whether an unknown sample is diabetes or not. For this purpose, PIMA diabetic detection for Female patients was used. Here 10 different classification model is used for prediction. Finally, the detailed performance analysis of the different variables of the PIMA dataset and also the classification model are discussed.

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Section: Literature Reviewmentioning

confidence: 86%

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Bandyopadhyay¹,

Bose²,

Goyel³

2021

Preprint

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“…Prediction models can screen pre-diabetes or people with an increased risk of developing diabetes to help decide the best clinical management for patients. Numerous predictive equations have been suggested to model the risk factors of incident diabetes [ 15 , 16 , 17 ]. For instance, Heikes et al [ 18 ] studied a tool to predict the risk of diabetes in the US using undiagnosed and pre-diabetes data, and Razavian et al [ 19 ] developed logistic regression-based prediction models for type 2 diabetes occurrence.…”

Section: Introductionmentioning

confidence: 99%

Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Joshi

Dhakal

2021

IJERPH

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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.

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“…The number of diabetes patients (aged over 18 years) has increased rapidly from 4.7 to 8.5% from 1980 to 2014 which imposes crucial challenges in both developed and developing nations [2]. It has been considered as the seventh major reason for the premature death rate and because of this only, 1.6 million people died every year [3]. Statistical studies reveal that in 2019, 463 million people are living with diabetes worldwide and it has been estimated to reach 578 million by 2030, and 700 million by 2045.…”

Section: Introductionmentioning

confidence: 99%

Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

Gupta

Varshney

Sharma

et al. 2021

Complex Intell. Syst.

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Background Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results The performance measures such as precision, accuracy, recall, F1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

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Deep learning approach for diabetes prediction using PIMA Indian dataset

Cited by 166 publications

References 31 publications

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

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