Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus

Singh, Namrata; Singh, Pradeep

doi:10.1016/j.bbe.2019.10.001

Cited by 80 publications

(39 citation statements)

References 72 publications

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“…Random forest combines 100 decision trees. RFC is a supervised form of learning that can deal with classification and regression problems [40].…”

Section: Random Forest Classifier (Rfc)mentioning

confidence: 99%

“…• Set decision trees for each sample, and the outcomes of each decision tree are predicted. • Select the most forecast outcome for the last forecast [40].…”

Section: Random Forest Classifier (Rfc)mentioning

confidence: 99%

“…This hyperplane divides space into various fields and contains a category of data for each domain. There are several hyperplanes to be selected to distinguish the two classes of data [40] [43] [51]. Table 3: SVM benefits and drawbacks [44] V. EXPERIMENTAL RESULTS AND DISCUSSION The results of applying the (RF) and (SVM) algorithms to the diabetes datasets, which are explained in detail in the" TABLE 1", were discovered using a web-based framework developed with Weka.…”

Section: ) Randommentioning

confidence: 99%

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Data Mining Classification Techniques for Diabetes Prediction

Abdulqadir

Abdulazeez

Zebari

2021

QAJ

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Diabetes may be predicted and prevented by exploring critical diabetes characteristics by computational data extraction methods. This study proposed a system biology approach to the pathogenic process to identify essential biomarkers as drug targets. The fact that disease recognition and investigation require many details, data mining plays a critical role in healthcare. This study aims to evaluate the efficiency of the methods used that are based on classification. Besides, the researchers have highlighted the most widely employed techniques and the strategies with the best precision. Many analyses include multiple Machine Learning algorithms for various disease assessments and predictions to improve overall issues. The detection and prediction of diseases is an aspect of classification and prediction. This paper estimates diabetes by its key features and also categorizes the relations between conflicting elements. The recursive random forest removal function provided a significant feature range. Random Forest Classifier investigated the diabetes estimate. RF offers 75,7813 greater precisions than Support Vector Machine (SVM).and may assist medical professionals in making care decisions.

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“…Random forest combines 100 decision trees. RFC is a supervised form of learning that can deal with classification and regression problems [40].…”

Section: Random Forest Classifier (Rfc)mentioning

confidence: 99%

“…• Set decision trees for each sample, and the outcomes of each decision tree are predicted. • Select the most forecast outcome for the last forecast [40].…”

Section: Random Forest Classifier (Rfc)mentioning

confidence: 99%

Section: ) Randommentioning

confidence: 99%

See 1 more Smart Citation

Data Mining Classification Techniques for Diabetes Prediction

Abdulqadir

Abdulazeez

Zebari

2021

QAJ

View full text Add to dashboard Cite

show abstract

“…The selection of the classifier used was based on the strength and weaknesses of the individual classifiers as revealed in the related works reviewed. After the training of the separate classifiers, cross-validation technique can be used for the training of the entire stacked architecture [33].…”

Section: A Stackingmentioning

confidence: 99%

“…Bagging is established on bootstrapping and aggregation approaches. Both bootstrapping and aggregation methods have gainful properties [33]. Bootstrapping comprises of acquiring irregular examples with substitution of a similar size as the first set.…”

Section: Bagging -Bootstrap Aggregationmentioning

confidence: 99%

Classifier Algorithms and Ensemble Models for Diabetes Mellitus Prediction: A Review

2021

IJATCSE

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Timely disease prediction means a lot to the improvement of the health care services and this will go a long way to assist populaces to avoid unsafe health circumstances before resulting in complex medical situations. Diabetes Mellitus as one of the deadly diseases that is described by hyperglycemia taking place due to defects in insulin secretion which allow an irregular increase in glucose level. Diabetes Mellitus can lead to loss of sight, non-traumatic lower extremity amputation, chronic kidney disease, coronary heart disease, stroke, etc. Hence, prompt diagnosis of the Diabetes Mellitus disease has to pay more attention to in the recent area of research. Presently, there is a great and wide work carried out on Machine Learning with a focus on medical and its application. This paper reviewed recent journalsthat made use of Artificial Intelligencetechniques, different classifiers and ensemble methods to assist in the management of diabetes. Classifiers algorithms such as Naive Bayes, Decision Tree, Artificial Neural Network, Support Vector Machine, K-Nearest Neighbour and Multi-Layer Perceptron. The results from over 1,137most related reviewed journalsreveled that Ensemble Models have the highest average accuracy of 87.09 % in respect to prediction of diabetes mellitus

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An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

Nagaraj

Deepalakshmi

2022

Int J Imaging Syst Tech

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Diabetes is one of the most common and hazardous diseases, which can affect almost every organ in the body. Diagnosis of diabetes requires determining all vital parameters related to the disease. However, the nature of the data from those parameters is very uncertain, affecting the process of disease diagnosis. This article proposes an intelligent fuzzy inference rule-based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes. The suggested model employs an inference technique that medical specialists have validated for recommendations. IFIR_PDDM comprises three elements used to forecast the risk of diabetes disease. Initially, a fuzzy membership function utilizes medical recommendations and statistical methodologies. Medical specialists then validate the mining-based rules using a decision tree rule induction technique. The proposed model predicts the risk of diabetes disease using fuzzy inference based on Mamdani's technique. Based on this information, the recommendations for a normal life, nutrition, exercise, and medications are given to patients. We used an electronic health record (EHR) medical and clinical dataset from the PIMA Indian Diabetes dataset to develop our proposed model and assess its performance. The proposed model takes less time for diabetes diagnosis, and the expert recommendation system uses the fuzzy inference method.

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Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus

Cited by 80 publications

References 72 publications

Data Mining Classification Techniques for Diabetes Prediction

Data Mining Classification Techniques for Diabetes Prediction

Classifier Algorithms and Ensemble Models for Diabetes Mellitus Prediction: A Review

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

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