Evaluating Machine Learning Methods for Predicting Diabetes among Female Patients in Bangladesh

Pranto, Md. Badiuzzaman; Mehnaz, Sk. Maliha; Mahid, Esha Bintee; Sadman, Imran Mahmud; Rahman, Ahsanur; Momen, Sifat

doi:10.3390/info11080374

Cited by 62 publications

(28 citation statements)

References 23 publications

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“…Their collected Kurmitola dataset comprises 181 instances to predict diabetes efficiently. The authors obtained the highest-performed model: RF and KNN for Pima Indian diabetes (PID) and Kurmitola datasets, respectively [16]. In this regard, RF and KNN models obtained the highest performance in accuracy values of 77.9% and 81.2%, precision values of 81% and 80%, F1score values: 84% and 88%, respectively, for dataset PID and Kurmitola.…”

Section: A Traditional Machine Learning Techniquesmentioning

confidence: 94%

“…In comparison, Pranto et al [16] utilized various ML classifiers: DT, KNN, RF, and NB to predict diabetes with a satisfactory result. They utilized two different diabetes datasets: PID and Kurmitola datasets, in which the Kurmitola dataset was obtained from Kurmitola General Hospital at Dhaka in Bangladesh to perform such above-said ML classifiers for predicting diabetes [16]. Their collected Kurmitola dataset comprises 181 instances to predict diabetes efficiently.…”

Section: A Traditional Machine Learning Techniquesmentioning

confidence: 99%

“…Their developed model generated an accuracy of 98% while predicting chronic liver disease. After examining the prior investigations, it has been revealed that various past studies included either for generating various dimension reduction approaches: attribute permutation and hierarchical clustering approach, binary firefly algorithm, cooperative coevolution technique, LDA, NCA, ReliefF, Chaotic Darcy optimization, CSO, KH, BFO included in [22], [28], [30], [32], [33], [34], [36], or employing various single ML classifiers: DT, SVM, RF, MLP, NB, LR, KNN, XGB, LGBM, SVM-linear included in [5], [9], [13], [14], [16], [17], [18], [21], [23], or developing several combined approaches consisting of various outlier detection and removal approaches along with the imbalance learning algorithms: cluster-based oversampling technique, DBSCAN with SMOTE, Isolation forest with SMOTETomek, IQR algorithm with SMOTE, Instance selection with SMOTE included in [10], [11], [12], [19], [26], or utilizing only single imbalance learning algorithms: SMOTE, SVM-SMOTE included in [20], [24], [25], [27], or implementing hyperparameter optimization strategies: 2level genetic optimizer with c-type SVM, LR with GA optimization strategy included in [29], [35], or implementing several DL-enabled techniques: Conv-LSTM, deep extreme learning model, LSTM, ANN, deep neural network, MLP, convergent artificial intelligence model, deep convolutional neural network, successive encoder-decoder approach, VAE, CLUSTIMP included in [2], [3], [37], [38], [39], [40], [41], [42], [44], [45], [46], [47], [48],…”

Section: Ensemble Learning Techniquesmentioning

confidence: 99%

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Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

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Section: A Traditional Machine Learning Techniquesmentioning

confidence: 94%

Section: A Traditional Machine Learning Techniquesmentioning

confidence: 99%

Section: Ensemble Learning Techniquesmentioning

confidence: 99%

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Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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“…They used different machine learning algorithms to perform the prediction. Finally shows the Naïve Bayes algorithm gives the best result among them [8].…”

Section: Literature Reviewmentioning

confidence: 98%

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Bandyopadhyay¹,

Bose²,

Goyel³

2021

Preprint

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Diabetics or Diabetic Mellitus is a metabolic disorder of blood sugar levels in the human body. It is a major non-communicable disease and involved many serious health risk issues. This disease is rapidly increasing in India. It is a chronic condition and it occurs when a body doesn't produce enough insulin hormone to control the blood sugar level. In this study, different variables have been analyzed that cause the diabetics, and different machine learning algorithms are used to predict whether an unknown sample is diabetes or not. For this purpose, PIMA diabetic detection for Female patients was used. Here 10 different classification model is used for prediction. Finally, the detailed performance analysis of the different variables of the PIMA dataset and also the classification model are discussed.

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“…The results of the study based on the above technique were 76.3% using discriminant analysis, 71.1% using the KNN algorithm, 76.1% using naive Bayes, 74.1% using SVM with linear kernel function, 74.1% using SVM with RBF kernel function. However, several authors [ 8 , 9 , 10 ] have used various methods in order to obtain the best prediction rate.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2

Nedyalkova

Madurga

Simeonov

2021

IJERPH

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A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status.

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Evaluating Machine Learning Methods for Predicting Diabetes among Female Patients in Bangladesh

Cited by 62 publications

References 23 publications

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2

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