Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

Yuvaraj, N.; SriPreethaa, K R

doi:10.1007/s10586-017-1532-x

Cited by 105 publications

(50 citation statements)

References 15 publications

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“…Veena Vijayan V. Anjali C. [1] proposed that uses AdaBoost algorithm with Decision Stump as base classifier for classification. Additionally Support Vector Machine, Naive Bayes and Decision Tree are also implemented as base classifiers for AdaBoost algorithm for accuracy verification.…”

Section: Related Workmentioning

confidence: 99%

“…While big data approach in healthcare is still in developing phase, it is clear that the designing of a healthcare platform for a better tomorrow will be of great help in enhancing the quality delivery of healthcare. According to the WHO report India is ranked Number one with 31.7 million number of diabetic patient in 2000 and is likely to increase up to 79.4 millionth [1]. Since there is a huge risk involved in the likelihood of increasing number of diabetic patient where accurate diagnosis will be the need of the hour [2].…”

Section: Introductionmentioning

confidence: 99%

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Survey on Predictive Analysis of Diabetes Disease Using Machine Learning Algorithms

A.Jamuna¹,

Priyadarsini²,

Titus³

2020

IJCSMC

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Big data analysis is predicated on large amount of data. Diabetes is caused due to the excessive amount of sugar condensed into the blood. One of the most critical chronic healthcare problems is diabetes.Undiagonosed diabetes problem may leads to damage eyes, heart, kidneys and nerves of diabetes patients. If improper medication taken is done which also lead to death. Early detection of diabetes is very important to maintain healthy life. Machine learning algorithm to identify a best predicting algorithm based various matrices such us accuracy, precision, recall, F-measure, sensitivity and specificity. This paper discusses about various ML techniques to predict the Diabetes disease by using dataset .Machine learning algorithm namely Decision Tree, SVM, Naive Bayes, Random forest, k-NN, K-mean clustering and LR algorithms are used in their experiment to detect diabetes at an early stage. Experiments are performed on Pima Indian diabetes dataset (PIDD) which is sourced from UCI Machine Learning repository. Result obtained show SVM outperform with high accuracy of 82% comparatively other algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey on Predictive Analysis of Diabetes Disease Using Machine Learning Algorithms

A.Jamuna¹,

Priyadarsini²,

Titus³

2020

IJCSMC

View full text Add to dashboard Cite

show abstract

“…Moreover, Yuvaraj and Sripreethaa [17] presented an application for diabetes prediction using three different ML algorithms including Random Forest, Decision Tree, and the Naïve Bayes. The Pima Indian Diabetes dataset (PID) was used after pre-processing it.…”

Section: Related Work Using Machine Learningmentioning

confidence: 99%

“…Moreover, these features were detected by different feature selection methods when the number of features is large. In fact, in [17,20,21], the authors used PIMA dataset with 13, eight and 49 attributes, respectively. When the feature selection methods were applied, these numbers were reduced to eight, four and nine features, respectively.…”

Section: Datasetsmentioning

confidence: 99%

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Current Techniques for Diabetes Prediction: Review and Case Study

et al. 2019

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Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML) techniques have been utilized in predicting diabetes in the last couple of years. The increasing complexity of this problem has inspired researchers to explore the robust set of Deep Learning (DL) algorithms. The highest accuracy achieved so far was 95.1% by a combined model CNN-LSTM. Even though numerous ML algorithms were used in solving this problem, there are a set of classifiers that are rarely used or even not used at all in this problem, so it is of interest to determine the performance of these classifiers in predicting diabetes. Moreover, there is no recent survey that has reviewed and compared the performance of all the proposed ML and DL techniques in addition to combined models. This article surveyed all the ML and DL techniques-based diabetes predictions published in the last six years. In addition, one study was developed that aimed to implement those rarely and not used ML classifiers on the Pima Indian Dataset to analyze their performance. The classifiers obtained an accuracy of 68%–74%. The recommendation is to use these classifiers in diabetes prediction and enhance them by developing combined models.

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Dawn of Big Data with Hadoop and Machine Learning

Singh¹,

Verma²

2022

Machine Learning and Data Science

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Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

Cited by 105 publications

References 15 publications

Survey on Predictive Analysis of Diabetes Disease Using Machine Learning Algorithms

Survey on Predictive Analysis of Diabetes Disease Using Machine Learning Algorithms

Current Techniques for Diabetes Prediction: Review and Case Study

Dawn of Big Data with Hadoop and Machine Learning

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