Predicting Diabetes Onset: An Ensemble Supervised Learning Approach

Nnamoko, Nonso; Hussain, Abir; England, David

doi:10.1109/cec.2018.8477663

Cited by 31 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Three different supervised machine learning techniques like Arti cial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression are used in the work done by Tejas N. Joshi et al [12] for detection of diabetes disease at the earliest. An ensemble based supervised learning approach is introduced by Nonso Nnamoko et al [13]. Along with this, several other classi ers are employed and their outputs are aggregated.…”

Section: Related Workmentioning

confidence: 99%

An automatic detection and classification of diabetes mellitus using CNN

Aswiga

Karpagam

et al. 2023

Soft Comput

View full text Add to dashboard Cite

Machine learning algorithms have recently attracted more attention in the medical eld and have been successfully used in a variety of medical applications. Machine learning is used to assist medical practitioners to solve complicated problems. Thus to function autonomously without seeking any assistance or help from a person, a well-known branch of machine learning called deep learning completes several related tasks parallely. It is successfully used in a variety of applications, including disease prediction and disease progression. Since tasks are assumed to be related to one another, existing learning methods adapts the Convolution neural network (CNN) to classify the diabetes mellitus disease effectively. To detect the abnormality, the proposed work uses a deep learning network that includes a novel CNN model that splits the data into separate training and testing sets before doing classi cation. Using CNN, the proposed work achieves better accuracy and outperforms well to the best of our knowledge.

show abstract

Section: Related Workmentioning

confidence: 99%

An automatic detection and classification of diabetes mellitus using CNN

Aswiga

Karpagam

et al. 2023

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Nnamokoetal [15] have collected a variety of factors that influence their potential to improve performance, particularly at the base level. The basis classifiers are chosen from five different machine learning algorithm families.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

Revathy¹,

Selvanayagi²

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

Machine Learning is concerned with the making of calculations and methods that use PCs to learn and acquire insight, using the related knowledge available.This work is focused on machine learning approaches for predicting diabetic disorders, using datasets from Predict the Diabetic Diseases. A web-based comparative analysis of multiple machine learning algorithms (Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression) is utilized in this paper, to assess their performances in recognizing reliable models for detecting diabetic disease. To see the effects of adding more features to the classification model, three performance measures were chosen: F1-Measure, Precision, and Accuracy.

show abstract

“…The authors also highlighted the significance of various classification methods used for disease prediction in medical datasets [24]. N. Nnamoko et al researched to predict diabetes and exploit diversity from heterogeneous base classifiers and the optimisation effect of attribute subset selection in order to improve accuracy [25]. In another work, authors predicted whether someone has diabetes or not.…”

Section: Disease Prediction Using Data Miningmentioning

confidence: 99%

A Survey on Data Mining Techniques for COVID Prediction

2021

IJETER

View full text Add to dashboard Cite

Corona Virus Disease of 2019 (COVID-19) has emerged as a serious health emergency worldwide. The symptoms of COVID-19 are un-detectable at early stage in most of the patients. It spreads from person to person very rapidly and causes severe sickness and loss of life in a number of cases if not treated early. Data mining techniques are very commonly being used in medical sector for detection and prediction of a variety of diseases and medical conditions of patients. A number of researchers are also working towards prediction of possibility of infection of COVID-19 among humans using machine learning techniques, specifically by applying data mining methods. In this paper, an extensive survey of available literature in the domain of prediction of COVID-19 infection and other diseases has been presented. This also includes survey on data mining techniques, models and various datasets.

show abstract

Predicting Diabetes Onset: An Ensemble Supervised Learning Approach

Cited by 31 publications

References 22 publications

An automatic detection and classification of diabetes mellitus using CNN

An automatic detection and classification of diabetes mellitus using CNN

Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

A Survey on Data Mining Techniques for COVID Prediction

Contact Info

Product

Resources

About