Prediction of diabetes using cost sensitive learning and oversampling techniques on Bangladeshi and Indian female patients

Pranto, Md. Badiuzzaman; Mehnaz, Sk. Maliha; Momen, Sifat; Huq, Syed Maruful

doi:10.1109/icitr51448.2020.9310892

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…There were many studies that proposed the implementation of oversampling methods for the prediction of heart disease, diabetes and obesity using the most common approach -the Synthetic Minority Oversampling Technique (SMOTE) [10,12,18,19]. SMOTE is a famous approach used for the construction of a classifier for the imbalance dataset.…”

Section: Related Workmentioning

confidence: 99%

“…The classifier showed that J48 Decision Tree had higher accuracy ratio in comparison with Naïve Bayes (NB). The evaluation metrics commonly used for the imbalanced classification include F1 score, accuracy, precision, recall and AUC [12,23].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, it is crucial to prioritize and enhance the reliability of the existing ML models for disease occurrence prediction. This issues was highlighted by Singh and Tawfik [11] and Pranto et al, [12] in their studies along with the significant improvements on the evaluation of the classification models using balance dataset.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Modelling for Imbalanced Dataset: Case Study of Adolescent Obesity in Malaysia

Nur Liana Ab Majid,

Syahid Anuar

2023

ARASET

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Obesity among adolescent is a public health issue with increasing burden of disease. Predicting imbalanced health data with Machine Learning may introduce bias and lead to diminished model performance. Misclassification in healthcare data could lead to misdiagnosing a patient or failing to detect a health issue when it is present. The purpose of this study is to predict adolescent obesity using machine learning along with implementation of multiple approaches on the imbalanced dataset. This study used secondary dataset from National Health and Morbidity Survey 2017. Samples 13 – 17 years were selected for the classification. SPSS V26 was used for data pre-processing, data cleaning, and data analysis. Meanwhile, Python language used for prediction and evaluation of the models. Approaches on the imbalanced dataset including resampling method (Random Oversampling, Random Under-sampling) and hybrid method (SMOTE and ADASYN) were implemented. This dataset was used for the formation of predictive models on ML algorithm including Artificial Neural Network, Decision Tree, K-Nearest Neighbour, Logistic Regression, Naïve Bayes, Random Forest and Support Vector Machine. The performance of each model was evaluated and compared using accuracy, precision, recall, F- score and Area under the Curve (AUC). Random Oversampling approached with Decision Tree Algorithm performs the best with accuracy (91.35%), precision (0.93), recall (0.91), F- score (0.91) and AUC (0.91) for the prediction of obesity among adolescent in Malaysia. The presented ML model development workflow along with the imbalanced techniques can be adapted to other health survey-based studies and may be valuable for developing other clinical prediction models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Machine Learning Modelling for Imbalanced Dataset: Case Study of Adolescent Obesity in Malaysia

Nur Liana Ab Majid,

Syahid Anuar

2023

ARASET

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“…The idea is to convert a cost in-sensitive learner to a cost-sensitive learner by increasing the number of costly class examples and reducing the number of non-costly classes. As a result, increasing the frequency of costly classes will increase its weight, which ultimately reflects its importance in the learning process, this technique includes: Over-sampling : includes increasing the number of costly class examples (Pranto et al , 2020; Le et al , 2018; Devi et al , 2021; Zhang et al , 2017). Under sampling: includes reducing the number of less costly class examples (Ma et al , 2017; Tyagi and Mittal, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…Over-sampling : includes increasing the number of costly class examples (Pranto et al , 2020; Le et al , 2018; Devi et al , 2021; Zhang et al , 2017).…”

Section: Related Workmentioning

confidence: 99%

Cost-sensitive meta-learning framework

Shilbayeh

Vadera

2021

JM2

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Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

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