Effective Prediction of Type II Diabetes Mellitus Using Data Mining Classifiers and SMOTE

Shuja, Mirza; Mittal, Sonu; Zaman, Majid

doi:10.1007/978-981-15-0222-4_17

Cited by 48 publications

(26 citation statements)

References 19 publications

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“…SMOTE is a popular method of machine learning used for oversampling [29] in which the minority class in a data set is generated by a synthetic example in the feature area based on the selected k-nearest neighbor (k-NN) from the minority class [21]. This practice has been adopted in several biomedical studies [4,[30][31][32][33][34][35][36]. Mohammed et al [34] used the SMOTE method for enriching the minority class and concluded that oversampling has a positive impact on the prediction models.…”

Section: Data Enrichmentmentioning

confidence: 99%

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Ullah¹,

Saleem²,

Jamjoom³

et al. 2021

J Med Internet Res

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Background The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise. Objective The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth. Methods This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets. Results The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases. Conclusions Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.

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Section: Data Enrichmentmentioning

confidence: 99%

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Ullah¹,

Saleem²,

Jamjoom³

et al. 2021

J Med Internet Res

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show abstract

“…[ Shuja et al,(2020)] proposed a two-phase classification model to solve the class imbalance problem for predicting type II diabetes. SMOTE was used to rebalance the data.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

INDJCSE

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The objective of this paper is to build a CKD prediction model using machine learning techniques that can predict the risk of chronic kidney disease (CKD) in patients with Cardiovascular Disease (CVD) or at high risk of CVD. CVD is associated with worsening of renal functions. But patients with CVD remains often underdiagnosed and undertreated for CKD because mostly the clinical diagnosis and treatment are single organ centered in earlier stages. Machine learning algorithms have been widely used to predict and classify diseases in healthcare. Healthcare data is often imbalanced. In this analysis, the CKD prediction model is built using CVD data with imbalanced distribution of positive and negative cases. The analysis involves three stages: Stage I involves selecting the best model based on performance metrics that support imbalanced class distribution without applying any resampling techniques. Stage II involves oversampling the training data of the minority class using Synthetic Minority Oversampling Technique (SMOTE) and stage III involves randomly under-sampling the training data of the majority class to solve the class imbalance. The experimental results show that the MLP (Multi-Layer Perceptron)-SMOTE model performs better in predicting CKD with a better F-score, recall, precision, G-mean, balanced accuracy and RUC-AUC when compared to other models.

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“…There are several studies that discussed diabetes diagnosis prediction based on data. Besides Pima Indian dataset [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], there is also data from Luzhou [4], Irvine [18], Kashmir [19,20], online questionnaire [21], and dr. Schorling [9,21]. There are various classification methods on diabetes diagnosis prediction like random forest, J48, naïve bayes (NB), support vector machine (SVM), logistic regression, neural network (NN), and K-Nearest Neighbors.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing Handling to Enhance Detection of Type 2 Diabetes Mellitus based on Random Forest

Ramadhan¹,

Adiwijaya²,

Romadhony³

2021

IJACSA

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Diabetes is a non-communicable disease that has a death rate of 70% in the world. Majority of diabetes cases, 90-95%, are of diabetes cases are type 2 diabetes which is caused by an unhealthy lifestyle. Type 2 diabetes can be detected earlier by using examination that contains diabetes-related parameters. However, the dataset does not always contain complete information, the distribution between positive and negative classes is mostly imbalanced, and some parameters have low importance to the decision class. To overcome the problems, this study needs to carry out preprocessing to improve detection precision and recall. In this paper, propose an approach on dataset preprocessing, which is applied to diabetes prediction. The preprocessing approach consists of the following process: missing value process, imbalanced data process, feature importance process, and data augmentation process. The data preprocessing process uses the median for missing value, random oversampling for imbalanced data, the Gini score in the random forest for feature importance, and posterior distribution for data augmentation. This research used random forest and logistic regression as classification algorithms. The experimental results show that the classification increased by 20% precision and 24% recall by applying proposed method and random forest method compared to without proposed method and random forest method.

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Effective Prediction of Type II Diabetes Mellitus Using Data Mining Classifiers and SMOTE

Cited by 48 publications

References 19 publications

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

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Preprocessing Handling to Enhance Detection of Type 2 Diabetes Mellitus based on Random Forest

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