A novel multistage ensemble approach for prediction and classification of diabetes

Simaiya, Sarita; Kaur, Rajwinder; Sandhu, Jasminder Kaur; Alsafyani, Majed; Alroobaea, Roobaea; alsekait, Deema mohammed; Margala, Martin; Chakrabarti, Prąsun

doi:10.3389/fphys.2022.1085240

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The authors perform an entropy calculation for each attribute, followed by an information gain calculation to determine the score of each attribute, and then they choose the four most important features that were used to diagnose diabetes mellitus. Several machine-learning techniques were applied to the PIMA dataset in a study for the early identification of diabetic mellitus [9]. To address missing values, records with missing values were removed, which decreased the dataset's cardinality.…”

Section: Literature Surveymentioning

confidence: 99%

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Avinash Bhagat, Tawseef Ahmed Teli

2024

jes

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Deep learning is a highly useful technique for the early identification of diabetes mellitus, according to the study done by numerous authors over the past few decades. By using pre-processing techniques on the dataset to get rid of various anomalies like over-fitting, under-fitting, redundancy, missing values, and non-significant features to make it more efficient for analysis, it is possible to increase the effectiveness of deep learning algorithms for diagnosing the disease. This work addresses the global problem of diabetes by exploring a revolutionary deep-learning method for early identification. Conventional convolutional neural network (CNN) models have drawbacks when used with numerical medical datasets, like this study's PIMA Indians Diabetes Database. The article suggests a technique for transforming numerical data into visual representations depending on feature relevance to get over this obstacle. This conversion makes it possible to use strong CNN models for diabetes early diagnosis. Classifying the created diabetic images after feeding them into CNN architectures that have already been trained on VGG16 and ResNet50. The promising outcomes with an accuracy of 97.19% demonstrate the possibility of the suggested strategy for improving diabetes detection and validating the effectiveness of diabetes imaging in obtaining an early diagnosis.

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Section: Literature Surveymentioning

confidence: 99%

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Avinash Bhagat, Tawseef Ahmed Teli

2024

jes

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“…For an early diagnosis of this disease, various parameters like plasma glucose concentration, serum insulin, age, blood pressure, and so on have been collected. The traditional-based diabetes prediction requires a prolonged time to analyze and to the final decision [6,7].…”

Section: Introductionmentioning

confidence: 99%

Diabetes Prediction and Classification Using Self-adaptive Evolutionary Algorithm with Convolutional Neural Network

2024

IJIES

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Globally, diabetes mellitus is the most dangerous disease and it is important to predict disease at an early stage to treat the disease. The learning-based algorithms play a significant part in supporting decision-making in diabetes prediction as well as diagnosis. Machine Learning (ML) based diabetes classification suffers from poor performance due to constraints such as limited labeled data and the challenge of data imbalance. Therefore, this research proposes the Self-adaptive Evolutionary Algorithm (SAEA) with Convolutional Neural Network (CNN) for the prediction and classification of diabetes. For validating the proposed method's effectiveness, the data is collected from benchmark datasets such as the Pima Indian Diabetes dataset (PIDD), Frankfurt Hospital, Germany, and North California State University (NCSU). Then, the min-max normalization is used for normalizing the data in the preprocessing step. The Chi-square-based feature selection technique is used for selecting the best feature for categorial features and finally, the SAEA with CNN is used for classification and it classifies the disease into diabetes and nondiabetes. The proposed method's effectiveness is estimated by using various matrices and it attains the accuracy of 99.87% and 99.99% by using PIDD and German datasets when compared to existing approaches like Deep Neural network (DNN), CNN, and Deep CNN (DCNN).

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Multistage transfer learning for medical images

Ayana,

Dese,

Abagaro

et al. 2024

Artif Intell Rev

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Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep learning algorithms for optimal performance in medical image analysis. This paper explores the growing demand for precise and robust medical image analysis by focusing on an advanced deep learning technique, multistage transfer learning. Over the past decade, multistage transfer learning has emerged as a pivotal strategy, particularly in overcoming challenges associated with limited medical data and model generalization. However, the absence of well-compiled literature capturing this development remains a notable gap in the field. This exhaustive investigation endeavors to address this gap by providing a foundational understanding of how multistage transfer learning approaches confront the unique challenges posed by insufficient medical image datasets. The paper offers a detailed analysis of various multistage transfer learning types, architectures, methodologies, and strategies deployed in medical image analysis. Additionally, it delves into intrinsic challenges within this framework, providing a comprehensive overview of the current state while outlining potential directions for advancing methodologies in future research. This paper underscores the transformative potential of multistage transfer learning in medical image analysis, providing valuable guidance to researchers and healthcare professionals.

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A novel multistage ensemble approach for prediction and classification of diabetes

Cited by 7 publications

References 46 publications

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Diabetes Prediction and Classification Using Self-adaptive Evolutionary Algorithm with Convolutional Neural Network

Multistage transfer learning for medical images

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