Diabetic analytics: proposed conceptual data mining approaches in type 2 diabetes dataset

Alalwan, Sinan Adnan Diwan

doi:10.11591/ijeecs.v14.i1.pp88-95

Cited by 20 publications

(16 citation statements)

References 11 publications

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“…These data can affect the classification process in data mining [preprocessing]. For this reason, before data analysis, and data classification, data cleaning is carried out first [9], [21].…”

Section: Data Cleaning and Selectionmentioning

confidence: 99%

Formulation of city health development index using data mining

Bertalya

Prihandoko²,

Setyowati³

et al. 2021

IJEECS

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Every five years Public Health Research publishes a Public Health Development Index that describes public health in Indonesia. The Public Health Development Index is measured using data from the Public Health Research and the National Socio-Economic Survey, and the Village Potential Survey which is obtained by surveying from sampling data. In fact, the Provincial and City Health Offices have health profile data reports every year. For this reason, this study analyzes existing health profile data using data mining techniques to obtain indicator data that are very influential in formulating the City Health Development Index. This City Health Development Index was successfully formulated by adopting the Model of Public Health Development Index in 2013 and using indicators from annual health profile data which obtained from the data mining process, i.e., Random Forest algorithm. The proposed model can be used as the annual report of a city to describe the health condition of that city. For the future research, the model can be adopted to measure some specific aspects of city health condition.

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Section: Data Cleaning and Selectionmentioning

confidence: 99%

Formulation of city health development index using data mining

Bertalya

Prihandoko²,

Setyowati³

et al. 2021

IJEECS

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“…Author Sinan Adnan Diwan Alalwan has carried out a detailed literature survey on different methods for predicting T2DM [6]. In his work, he has suggested random forest method and self-organizing map for improving the accuracy of prediction.…”

Section: Related Workmentioning

confidence: 99%

Hybrid ANFIS-GA and ANFIS-PSO Based Models for Prediction of Type 2 Diabetes Mellitus

Patil¹,

Tamane

Rawandale

2020

Computational Methods and Data Engineering

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Type-Diabetes Mellitus (T2DM), a major threat to developing as well as developed countries, can be easily controlled to a large extent through lifestyle modifications. Diabetes increases the risk of developing various health as well as financial problems to cure these health complications. The health complications are stroke, myocardial infarction, and coronary artery disease. Nerve, muscle, kidney and retinal damage have distressing impact on the life of a diabetic patient. It is the need of the hour to halt the epidemic of T2DM in the early stage. Data science approaches have the potential to predict on medical data. Machine learning is an evolving scientific field in data science where machines learn mechanically and improve from experience without any explicit program. Our goal was to develop a system which can improve performance of a classifier for prediction of T2DM. The purpose of this work is to implement a hybrid model for prediction by integrating the advantages of artificial neural net (ANN) and fuzzy logic. Genetic algorithm (GA) and particle swarm optimization (PSO) have been applied to optimize parameters of developed predicting model. The proposed scheme used a fuzzification matrix. This matrix is used to relate the input patterns with a degree of membership to different classes. The specific class is predicted based on the value of degree of membership of a pattern. We have analyzed the proposed method and previous research in the literature. High accuracy was achieved using the ANFIS-PSO approach.

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“…The last stage of data mining is applying the algorithms and techniques of data mining to get the pattern, that the user looking for. The outcome of the applying data mining techniques will be represented as graph or table or other format of output representation [1,16].…”

Section: Data Mining Processmentioning

confidence: 99%

Performance evaluation of different classification techniques using different datasets

Özdemir

Yavuz

Dael

2019

IJECE

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<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>

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Diabetic analytics: proposed conceptual data mining approaches in type 2 diabetes dataset

Cited by 20 publications

References 11 publications

Formulation of city health development index using data mining

Formulation of city health development index using data mining

Hybrid ANFIS-GA and ANFIS-PSO Based Models for Prediction of Type 2 Diabetes Mellitus

Performance evaluation of different classification techniques using different datasets

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