Data Augmentation Using Gaussian Mixture Model on CSV Files

Arora, Ashish; Shoeibi, Niloufar; Sati, Vishwani; González‐Briones, Alfonso; Chamoso, Pablo; Corchado, Emilio

doi:10.1007/978-3-030-53036-5_28

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…e treatment aims at blood sugar level, insulin therapy, diet, and exercise. Some of the complications for type 1 include damage in the kidneys, problems in pregnancy and skin, blood flow becoming weak, problems in skin [5].…”

Section: Diabetes Is Majorly Classi Ed Into 4 Typesmentioning

confidence: 99%

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Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Ahamed

Arya

Nancy

2022

Advances in Human-Computer Interaction

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The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.

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Section: Diabetes Is Majorly Classi Ed Into 4 Typesmentioning

confidence: 99%

“…e diagnosis involves tests such as A1C test, FPG (fasting plasma glucose) test, and RPG (random plasma glucose) test. Other tests include oral glucose tolerance test (OGTT), glucose challenge test, fasting blood sugar (FBG) test, and random blood sugar (RBG) test [5].…”

Section: Type 2 Diabetesmentioning

confidence: 99%

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Ahamed

Arya

Nancy

2022

Advances in Human-Computer Interaction

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“…In order to obtain the value of the coefcient, the loss function used needs to be calculated. It is calculated using 2 , where y1 is the actually calculated value and y1 ′ is the fnally value that is predicted by the model. So y1 ′ is replaced with G n (X) which represents the target value [24].…”

Section: Gradient Boostingmentioning

confidence: 99%

“…However, the people going to be afected has been predicted to be increasing and might become double the number by the year 2045 as per research. To avoid and overcome the problem, the Prediction of Diabetes is essential [2]. By predicting Diabetes in the earlier stages, the number of people being afected can be reduced, and also timely medication will eliminate the disease earlier than expected [3].…”

Section: Introductionmentioning

confidence: 99%

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Ahamed

Arya

Sangeetha

et al. 2022

Applied Computational Intelligence and Soft Computing

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The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms.

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“…The GMM is a very popular unsupervised learning technique. This technique is popular to be used to form new synthetic data on small-sized datasets [17], [18], [19], [20]- [24]. Moreover, the GMM has been widely successfully applied in the prediction system on medical-related topics such as predicting COVID-19 cases [22], liver cancer detection [25], pancreatic cancer detection [26], medical image segmentation [27], and texture characterization of brain DTI image [28].…”

Section: B Feature Selection and Data Augmentationmentioning

confidence: 99%

Impact of Feature Selection and Data Augmentation for Pregnancy Risk Detection in Indonesia

Basuki¹,

Irfan²,

Azhar³

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper aims to develop an automatic system for pregnancy risk detection in Indonesia. The system requires a sophisticated approach to achieve the required performance as a sensitive field. Existing works are developed using small-sized datasets and limited classification features. Moreover, all features treated equally make the detection results hard to interpret which features contribute more. To address these issues, we propose to combine more complex features, data augmentation methods, and feature selection techniques. We prefer to use all 118 pregnancy indicators and 400 instances from Puskesmas as an original dataset. Next, the new datasets are used to build two data augmentation methods, i.e., GMM and CTGAN. Each data augmentation method generates 2,000 new synthetic instances. Following this, five machine learning methods combined with three feature selection approaches, i.e., RFE, Random Forest, and Chi-Square, are implemented in all datasets. Through experiments, we observed that feature selection techniques play an essential role in improving classification accuracies. While the GMM-based augmentation demonstrated performance improvement, the CTGAN-based synthetic dataset depicted low performances. The best accuracy on all experiment settings reached 95%. By using Random Forest combined with RFE on a GMM-based dataset, the highest accuracy was achieved using only five features. Another notable result is that both XGBoost and Decision Tree reached the same 95% accuracy on the GMM-based dataset on only nine features. The overall results show that appropriate data augmentation and feature selection are a matter for achieving better performance in this research.

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Data Augmentation Using Gaussian Mixture Model on CSV Files

Cited by 12 publications

References 15 publications

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Impact of Feature Selection and Data Augmentation for Pregnancy Risk Detection in Indonesia

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