Predictive modelling and analytics for diabetes using a machine learning approach

Kaur, Harleen; Kumari, Vinita

doi:10.1016/j.aci.2018.12.004

Cited by 215 publications

(126 citation statements)

References 20 publications

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“…The Machine Learning is a powerful procedure that permits computers toward learning the information from both unstructured and structured data (Kaur and Kumari 2018). On the other hand, it permits the system toward learning and gains intelligence based on the earlier input data.…”

Section: Overview Of Machine Learningmentioning

confidence: 99%

“…Most of the researcher suggested various recognition and classification method like artificial neural network, Naïve Bayesian, support vector machine, logistic regression and so on (Kaur and Kumari 2018). But till date, we are facing the problem during the extraction of hidden and useful data from huge set of multi-dimensional medical repositories.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

Radhika*,

Priya

2020

IJITEE

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Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

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Section: Overview Of Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

Radhika*,

Priya

2020

IJITEE

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“…Supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbor (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR) are used for this purpose. [2] Juan A. Botia, Sebastian Guelfi, in their journal, "G2P: Using machine learning to understand and predict genes causing rare neurological disorders", the genes that cause rare neurological disorders can be understood and predicted using Machine Learning approaches. One can get excellent demonstration of explanatory as well as predictive power of machine learning based models in neurological diseases in this research.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Approach for Identification of Diseases through Gene Mapping

Belapurkar¹

2019

IJRASET

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Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Genetic mapping is based on the use of Genetic Techniques to construct maps showing the positions of genes and other sequence features on genome. The early applications of machine learning to population genetics demonstrate that they outperform traditional approaches. Potentially important disease biomarkers have been revealed by the use of machine learning methods on gene expression data, where algorithms learn to differentiate between different disease phenotypes. Genetic testing can be considered as the perfect field for machine learning applications in many ways, considering the enormous amount of data that these programs need to contend with.

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“…Advanced diabetes is complicated by coronary illness, visual impairment, and kidney failure [1,2]. Early detection of the disease can give patients the opportunity to make the necessary lifestyle changes and therefore can improve their life expectancy [3].…”

Section: Introductionmentioning

confidence: 99%

Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods

et al. 2019

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(1) Background: Diabetes is a common chronic disease and a leading cause of death. Early diagnosis gives patients with diabetes the opportunity to improve their dietary habits and lifestyle and manage the disease successfully. Several studies have explored the use of machine learning (ML) techniques to predict and diagnose this disease. In this study, we conducted experiments to predict diabetes in Pima Indian females with particular ML classifiers. (2) Method: A Pima Indian diabetes dataset (PIDD) with 768 female patients was considered for this study. Different data mining operations were performed to a conduct comparative analysis of four different ML classifiers: Naïve Bayes (NB), J48, Logistic Regression (LR), and Random Forest (RF). These models were analyzed by different cross-validation (K = 5, 10, 15, and 20) values, and the performance measurements of accuracy, precision, F-score, recall, and AUC were calculated for each model. (3) Results: LR was found to have the highest accuracy (0.77) for all ‘k’ values. When k = 5, the accuracy of J48, NB, and RF was found to be 0.71, 0.76, and 0.75. For k = 10, the accuracy of J48, NB, and RF was found to be 0.73, 0.76, 0.74, while for k = 15, 20, the accuracy of NB was found to be 0.76. The accuracy of J48 and RF was found to be 0.76 when k = 15, and 0.75 when k = 20. Other parameters, such as precision, f-score, recall, and AUC, were also considered in evaluations to rank the algorithms. (4) Conclusion: The present study on PIDD sought to identify an optimized ML model, using with cross-validation methods. The AUC of LR was 0.83, RF 0.82, and NB 0.81). These three were ranked as the best models for predicting whether a patient is diabetic or not.

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Predictive modelling and analytics for diabetes using a machine learning approach

Cited by 215 publications

References 20 publications

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

Machine Learning Approach for Identification of Diseases through Gene Mapping

Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods

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