Diagnosis of diabetes using classification mining techniques

Iyer, Aiswarya; Jeyalatha, S.; Sumbaly, Ronak

doi:10.48550/arxiv.1502.03774

Cited by 5 publications

(5 citation statements)

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“…It has been widely used for classification and prediction in many domains because it can be constructed quickly and easily from data. Also, it reduces space complexity, allowing quick inference, and often outperforms more complex learning algorithms in practice [97,98]. The Naive Bayes Classifier provides a highly efficient probability estimation based on a simple structure, requiring only a small amount of training data to predict the classification parameters.…”

Section: Plos Onementioning

confidence: 99%

Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia

Alazwari

Johnstone²,

Tafakori³

et al. 2023

PLoS ONE

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The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (χ2 = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow’s milk (OR = 2.92, P = 0.000), birth weight >4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age >25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia.

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Section: Plos Onementioning

confidence: 99%

Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia

Alazwari

Johnstone²,

Tafakori³

et al. 2023

PLoS ONE

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“…There are different parameters based on which the accuracy of the results is defined stated below: Relative Absolute Error: It is the way that provides the measure of the performance of a trained or predictive model used in data mining and machine learning. It is a general measure of accuracy or precision expressed as a ratio as a result of comparing a mean error to errors produced by the naïve model (Iyer et al, 2015).…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Risk Prediction of Thalassemia Using Data Mining Classifiers

Ali,

Saqib

2023

Am. J. Med. Sci. Innov.

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Medical data mining is concerned with prediction knowledge, which is a useful method for extracting hidden patterns from given data for specific purposes. Thalassemia is one of the most common inherited blood hematological disorders, and this paper adopted data mining classification techniques to generate results with high performance and accuracy for risk prediction of thalassemia. The dataset for this purpose was collected from NIBD (National Institute of Blood Diseases), a well-known institute and hospital for blood diseases in Karachi, Pakistan. They provided 301 records of CBC test reports containing positive and negative statuses of diagnosis of thalassemia traits. There were many instances in the report, of which 6 were used for our research purpose, i.e. Gender, MCV, HGB, HCT, MCHC, and RDW. The dataset was divided into training and test data using the WEKA tool. Four algorithms of data mining classification, namely J48 Decision Tree, Naïve Bayesian Network, SMO algorithm, and Multilayer Perceptron Neural Network were adopted to train the model and classify the patient having traits of thalassemia from normal persons with the use of the WEKA tool. Results revealed that out of all four algorithms, Naïve Bayes provided results with the highest accuracy of 99%.

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“…In reference [2], the diagnosis of diabetes has been made by analyzing data in patterns and classifying using a Decision Tree (DT). In this study, the DT algorithm is responsible for analyzing the patterns in the data, and the results of the evaluations show an accuracy of 79.86%.…”

Section: Related Workmentioning

confidence: 99%

Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm

Rezaei¹,

Abbasitabar²,

Mirzaei³

et al. 2022

J Big Data

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Today's lifestyle has led to a significant increase in referrals to medical centers to diagnose various diseases. To this end, over the past few years, researchers have turned to new diagnostic methods, including data mining and artificial intelligence, intending to facilitate the detection process and increase reliability. The high volume of data available in medical centers can be considered one of the main problems in using these methods. The optimal selection of essential and influential features reduces the maximum dimension for better diagnosis with more reliability of results. In this paper, a new approach uses a Binary Exchange Market Algorithm (BEMA) to identify essential and practical features in the diabetes dataset and determine the best algorithm binary function (type of sigmoid function) to improve the performance of the EMA algorithm is presented. For validation and efficiency of the proposed BEMA algorithm, several SVM, KNN, and NB classification models have been used to train and test the final model. The results obtained from the evaluations show that the proposed BEMA-SVM combined method has a better performance than the previous methods to improve accuracy and offer an effect equivalent to 98.502%. Also, to provide better results and more reliability than the proposed method, researchers can use a combination of several classes with the proposed method, which is outside the scope of this study.

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Diagnosis of diabetes using classification mining techniques

Cited by 5 publications

References 0 publications

Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia

Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia

Risk Prediction of Thalassemia Using Data Mining Classifiers

Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm

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