Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus

Vijayan, V. Veena; Ravikumar, Aswathy

doi:10.5120/16685-6801

Cited by 41 publications

(15 citation statements)

References 9 publications

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“…In [2], different methods are compared for diabetes detection. Data mining techniques are explored in [8,9] for detection of diabetes. A clinical system is proposed and implemented in [3] for diagnosis of diabetes.…”

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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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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“…It was also claimed that HMC and double crossover genetic process based methods were shown better performance to compare other scales. Vijayan et al showed that K-Nearest Neighbor (KNN), K-means, amalgam KNN and ANFIS were used to predict and diagnosis DM [13]. For maximizing expectation in a successive iteration cycle, they used EM algorithm for sampling diabetes data.…”

Section: Related Workmentioning

confidence: 99%

Mining Significant Features of Diabetes Mellitus Applying Decision Trees: A Case Study In Bangladesh

Howladar

Satu

Barua

et al. 2018

Preprint

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Diabetes is a chronic condition which is associated with an abnormally high level of sugar in the blood. It is a lifelong disease that causes harmful effects in human life. The goal of this research is to predict the severity of diabetes and find out significant features of it. In this work, we gathered diabetes patients records from Noakhali Diabetes Association, Noakhali, Bangladesh. Thus, We preprocessed our raw dataset by replacing and removing missing and wrong records respectively. Thus, CDT, J48, NBTree and REPtree decision tree based classification techniques were used to analyze this dataset. After this analysis, we evaluated classification outcomes of these decision tree classifiers and found the best decision tree model from them. In this work, CDT unpruned tree shows highest accuracy, precision, recall, f-measure, second highest AUROC and lowest RMSE than other models. Then, we extracted possible rules and significant features from this model and plasma glucose, plasma glucose 2hr after glucose and HDL-cholesterol have been found as the most significant features to predict the severity of Diabetes Mellitus. We hope this work will be beneficial to build a predictive system and complementary tool for diabetes treatment in future.

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“…Applications using data mining techniques are widely designed, such as the specific disease predicting system for hospital database. Karabatak designed an expert system based for detection of the breast cancer based on association rules and neural network, the system was applied to the Wisconsin breast cancer database [7]. Vijayanv V analyzed and compared some novel data mining algorithms for prediction and diagnose of diabetes mellitus, such as, EM, KNN, K-means, ANFIS and so on, and experimented on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of Machine Learning databases [8].…”

Section: Introductionmentioning

confidence: 99%

Fasting Blood Glucose Change Prediction Model Based on Medical Examination Data and Data Mining Techniques

Xao

Shao

et al. 2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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Fasting blood glucose (FBG) is an important indicator for human's health. Prediction for FBG is meaningful for findingand healing diseases, especially for diabetes mellitus. Based on four years' historical medical examination data, a prediction model of coming year's FBG is presented using traditional data mining techniques with a novel algorithm to estimate the FBG change probability and a proposed feature selection algorithm, which combines the feature importance scores of ensemble learning and Sequential Backward Selection (SBS) algorithm to select an optimal feature subset. Experimental data are collected from a medical examination database containing 108,386 users, in which 7,136 people have four years' records. Compared with traditional support vector machine (SVM) and random forest, experimental results demonstrate that the feature selection algorithm can improve the performance of both SVM and random forest. Also the proposed method to estimate the probability of the FBG change works promisingly for giving an intuitive description of predictive result.

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Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus

Cited by 41 publications

References 9 publications

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

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

Mining Significant Features of Diabetes Mellitus Applying Decision Trees: A Case Study In Bangladesh

Fasting Blood Glucose Change Prediction Model Based on Medical Examination Data and Data Mining Techniques

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