Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression

Resti, Yulia; Kresnawati, Endang Sri; Dewi, Novi Rustiana; Zayanti, Des Alwine; Eliyati, Ning

doi:10.26554/sti.2021.6.2.96-104

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Let n X d Y j is the number of images related to the j-th class in all variables X, n Y j is the number of images in the j-th class, n c X d Y j is the number of images related to the j-th class in a variable X d with category k, and m is the number of categories in the variable X d . The j-th class prior probability and the j-th likelihood function, respectively, are defined as [14,41]:…”

Section: Multinomial Naïve Bayesmentioning

confidence: 99%

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Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

et al. 2023

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As an agricultural commodity, corn functions as food, animal feed, and industrial raw material. Therefore, diseases and pests pose a major challenge to the production of corn plants. Modeling the classification of corn plant diseases and pests based on digital images is essential for developing an information technology-based early detection system. This plant’s early detection technology is beneficial for lowering farmers’ losses. The detection system based on digital images is also cost-effective. This paper aims to model the classification of corn plant diseases and pests based on digital images by implementing fuzzy discretization. Discretization is an essential technique to improve the knowledge extraction process of continuous-type data. It is also essential in some methods where continuous data must be processed or handled. Fuzzy discretization allows classes to have overlapping intervals so that they can handle information that is vague or unclear. We developed hypotheses and proved that different combinations of membership functions in fuzzy discretization affect classification performance. Empirical assessment using Monte Carlo resampling was carried out to obtain the generalizability of the performance of the best classification model of all proposed models. The best model is determined based on the number of metrics with the highest value and the highest metric on the Fscore and Kappa, a multiclass measure. The combination of digital image data preprocessing and classification methods also affects the performance of the classification model. We hope this work can provide an overview for experts in building early detection systems of corn plant diseases and pests using classification models based on fuzzy discretization.

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Section: Multinomial Naïve Bayesmentioning

confidence: 99%

“…Since the product of the predictor variable prior probability is a constant for each class, the posterior probability is written as [14,41]:…”

Section: Multinomial Naïve Bayesmentioning

confidence: 99%

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

et al. 2023

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“…About 400 persons were included in the database, which came from all across the world. And in Resti et al [9] a model validation built which based on 5-fold cross-validation, which divided the data into training and test data. The Gaussian Nave Bayes was the best strategy for predicting diabetes diagnosis, according to the model validation results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Gestational DM (GDM) is a type of glucose intolerance that develops during pregnancy and can cause difficulties for both the mother and the fetus [8]. Women are also more likely to have diabetes-related comorbidities such as renal disease, depression, and poor vision [9]. It can be detected early, which reduces the patient's health risk [10].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent and Precise Method Used for Detecting Gestational Diabetes in the Early Stages

Hameed¹,

Ali²

2022

UHD J SCI TECH

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This paper suggests a Naive Bayes classifier technique for identifying and categorizing gestational diabetes mellitus (GDM), GDM is a kind of diabetes mellitus that affects a small proportion of pregnant women but recovers to normal once the baby is born. The Pima Indians Diabetes Dataset was chosen for a comprehensive analysis of this critical and pervasive health disease because it contains 768 patient characteristics acquired from a machine learning source at the University of California, Irvine. The goal of the study is to apply smart technology to categorize diseases with high accuracy and precision, practically free of conceivable and potential faults, to provide satisfying findings. The approach is based on eight major characteristics that are present in the operations that are required to establish a precise and reliable categorization system. This approach involves training and testing on real data, as well as for deciding whether or not to construct a categorization model. The work was compared to earlier work and had a 96% accuracy rating.

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“…For continuous predictor variables, algorithm C4.5 of this model makes decisions by splitting the predictor variables locally (Quinlan, 1996) , and the performance of this model increases signi cantly if the continuous predictor variables are discretized rst (Dougherty et al, 1995) . In many cases, the implementation of this model has performed well (Kresnawati et al, 2021;Resti et al, 2021;Hussein et al, 2020), including classifying plant diseases using digital images (Rajesh et al, 2020;Kranth et al, 2018). However, in some other cases, the implementation of this model does not provide satisfactory performance (Xian and Ngadiran, 2021;Sahith et al, 2019;García et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Resti

Irsan

Amini

et al. 2022

Sci. Technol. Indones

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Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

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Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression

Cited by 11 publications

References 24 publications

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

An Intelligent and Precise Method Used for Detecting Gestational Diabetes in the Early Stages

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

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