Analyzing Diabetic Data using Classification Algorithms in Data Mining

Saravananathan, K.; Velmurugan, T.

doi:10.17485/ijst/2016/v9i43/93874

Cited by 40 publications

(14 citation statements)

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“…Difference performance metrics were computed. Authors found outstanding classification rate with J48 as compared to other classification techniques [8].…”

Section: Figure 1 : Different Diseases and The Death Ratementioning

confidence: 94%

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Sharma

Singh

2018

ICST Transactions on Scalable Information Systems

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The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.

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“…Difference performance metrics were computed. Authors found outstanding classification rate with J48 as compared to other classification techniques [8].…”

Section: Figure 1 : Different Diseases and The Death Ratementioning

confidence: 94%

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Sharma

Singh

2018

ICST Transactions on Scalable Information Systems

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“…Among those methods J48 produce the highest accuracy about 67.16%. They state that to improve the overall accuracy they need more data set [9]. Diabetes can be caused by obesity, being overweight and being inactive.…”

Section: Literature Reviewmentioning

confidence: 99%

Diabetes Mellitus Prediction using Classification Techniques

Hassan¹,

Malaserene²,

Leema³

2020

IJITEE

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Diabetes is a metabolic disease affecting people in almost every country and it may lead to severe problems like stroke, kidney failure or premature death if it is not predicted at the early stage. To mitigate this many researchers are working to predict the diabetes at early stage using several methods. Different accessible conventional techniques are carried out to diagnose diabetes depend on physical and substance tests. Several data mining methods were designed to overcome these uncertainties. Classification techniques like Decision Tree, KNearest Neighbors, and Support Vector Machines are used to classify the patients with diabetes mellitus. The performance of these applied techniques are determined using the factors precision, accuracy, Sensitivity, and Specificity. The results obtained proved that SVM outperforms decision tree and KNN with highest accuracy of 90.23%. Performance analysis of these classification methods helps us to decide which appropriate technique to choose in future for analysing the given dataset.

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“…A survey of different methods used in diagnosis of diabetes is made in [27]. Similar kind of study is found in [28], while [29] throws light into analysis of diabetes data and [30] focuses on outlier detection in the process of predicting diabetes. Medical data can be subjected to classification using computational intelligence approaches.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Diagnosing Kidney Disease in Diabetes Patients Using Classification Algorithms

Prasad¹,

Reddy²,

Puneeth³

2020

SN COMPUT. SCI.

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Data mining algorithms are widely used to extract latent trends or patterns from databases. They are used to predict unknown class labels in order to help in accurate decision making process. The machine learning techniques are very useful in applications of healthcare domain. In this domain, the techniques are employed to diagnose diseases and even predicting the probabilities related to health and other aspects. Since diabetes is one of the diseases that prevail in Indians now and it causes other diseases as well, in this paper we studied the kidney disease diagnosis in diabetes patients. Kidney disease dataset is used to explore four different data mining algorithms. They are known as Naive Bayes, decision table, J48 and random forest. The dataset is used to have both training and testing sets and produce prediction details. These algorithms are evaluated by using measures like accuracy, mean absolute error (MAE) and root-mean-squared error (RMSE). The results revealed that all the machine learning algorithms are able to predict the disease. However, they differ in accuracy levels. With tenfold cross-validation, it is understood that random forest showed the highest accuracy, while Naive Bayes showed least MAE and RMSE. We built prototype application to demonstrate proof of the concept. Our experimental results revealed that the proposed framework to evaluate machine learning algorithms is useful.

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Analyzing Diabetic Data using Classification Algorithms in Data Mining

Cited by 40 publications

References 1 publication

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Diabetes Mellitus Prediction using Classification Techniques

A Framework for Diagnosing Kidney Disease in Diabetes Patients Using Classification Algorithms

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