Machine Learning Based Unified Framework for Diabetes Prediction

Mahmud, Sakib; Hossin, Md Altab; Ahmed, Md. Razu; Noori, Sheak Rashed Haider; Sarkar, Nazirul Islam

doi:10.1145/3297730.3297737

Cited by 38 publications

(7 citation statements)

References 18 publications

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“…Multilayer Perceptron (MPL) and J48 classifier techniques in the Weka tool recently being used in successfully predicting malaria incidents [32][33][34]. Researchers around the globe also used it for prediction of dengue [35][36][37][38] and other public health issues such as Cholera [39], diabetes [40][41][42], heart diseases [43,44].…”

Section: Data Preparationmentioning

confidence: 99%

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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Background: India has a rising rate of malaria as well as a high mortality rate despite awareness and efforts being focused on the issue. Some regions are profoundly affected than others, such as in Odisha, where the prevalence of malaria is nearly a third of the whole country. This study investigated the influence of climate factors on the incidence of malaria in the Sundargarh district in the state of Odisha, India. Methods: Block-wise observed station rainfall data was sourced from the Special Relief Commissioners' (SRC) web portal. Gridded surface maximum temperature and relative humidity data were accessed from the European Center for Medium-range Weather Forecast (ECMWF) reanalysis data archive. Malaria incident data were collected from the Directorate of Public Health, Government of Odisha. WEKA machine learning tool with two classifier techniques, Multi-Layer Perceptron (MLP) and J48 with 10-fold cross-validation, percentile split (66%), and supplied test options, were used for the Malaria prediction. A comparative analysis was carried out on both techniques to ascertain the superior model amongst the two, concerning the prediction accuracy of malaria in the context of a varying climate. Classifier accuracy, Root Mean Square Error (RMSE), Kappa, and ROC scores were the indicators used for the analysis. Results: The results suggested that J48 had exhibited a better skill to MLP and illustrated less error with a positive kappa. In particular, the 10-fold cross-validation method had better performance over the percentile Spilt (66%) and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Further, seasonal temperature and humidity variation had shown a better association with malaria incidents in comparison to rainfall.Conclusion: The performance of the machine learning methods for Sundargarh was particularly better during the monsoon and post-monsoon when the events are at the peak. The results were encouraging for the utilization of climate forecast for prediction of malaria incidences. It is thus recommended that the J48 classifier machine learning technique could be adopted for the development of malaria early warning system.

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Section: Data Preparationmentioning

confidence: 99%

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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show abstract

“…If (f ) function is completely monotone in (0,1), then the power series expansion of (f ) function in (0,1) has to be alternating because (−1) k f k ≥ 0. On the other hand, consider an alternating power series of function f (x) converging for all 0 < x < 1 and its derivatives by Equations (14)- (16):…”

Section: Logistic Regression Forecastingmentioning

confidence: 99%

“…Parampreet et al applied a cloud-based framework with the help of sensor devices to initially screen patients for the prediction of diabetes [15]. Further, Hassan et al proposed a unified machine-learning framework for diabetes predications in big data [16]. There is considerable interest in determining how different classification techniques from machine learning can be utilized as disease prediction tools [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria

et al. 2019

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The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes.

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“…Hasan Mahmud et al [12] proposed a framework for predicting diabetes using ML algorithms. The pima dataset from UCI repository is considered for the work.…”

Section: Literature Surveymentioning

confidence: 99%

Evolving A Neural Network to Predict Diabetic Neuropathy

Reddy

Mahesh

Nanjundan³

2018

ICST Transactions on Scalable Information Systems

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One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The main aim of this work is to identify various risk factors of DN and predict it accurately using ML techniques. Radial basis function (RBF) network is an artificial neural network proposed to obtain better results than traditional ML classification techniques. CART, random forest and logistic regression are existing classification techniques considered. Accuracy, recall, f1 score, area under ROC curve (AUC), Matthews correlation coefficient (MCC) and kolmogorov-smirnov (KS) statistic are performance metrics used to evaluate and compare algorithms. From comparative study it was observed that proposed technique RBF network performed better. The performance metric values obtained for RBF network are accuracy-68.18%, recall-0.909, f1score-0.7407, AUC-0.6405, MCC-0.4082 and KS statistic-0.5417. Accordingly, the use of RBF network while predicting DN gives accurate and better results.

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Machine Learning Based Unified Framework for Diabetes Prediction

Cited by 38 publications

References 18 publications

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria

Evolving A Neural Network to Predict Diabetic Neuropathy

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