Prediction of Chances - Diabetic Retinopathy using Data Mining Classification Techniques

Ananthapadmanabhan, K. R.; Parthiban, G.

doi:10.17485/ijst/2014/v7i10.19

Cited by 18 publications

(5 citation statements)

References 8 publications

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“…K. R. Ananthapadmanaban etal (2014) proposed the chance of occurrence of diabetic retinopathy based on classification using data mining methods [8]. In this work, diabetic retinopathy has been diagnosed earlier.…”

Section: Literature Reviewmentioning

confidence: 94%

Diabetic Retinopathy Feature Extraction and Classification using Adaptive Super Pixel Algorithm

Subramanian¹,

Saravanan²,

Nayak³

et al. 2019

IJEAT

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Diabetic Retinopathy is an ocular manifestation of diabetes . The longer a person has diabetes, higher are the chances of having diabetic retinopathy in their visual system. Hence the objective of this research work is to propose an automated, suitable and sophisticated approach using image processing so that diabetic retinopathy can be detected at early levels easily and damage to retina can be minimized. A vital point of diabetic retinopathy that it causes detectable changes in the blood vessels of the retina. The focal blurred edges are detected so as to dismiss the false alarms. A two-level approach is used here to classify data. Firstly, optimal features are extracted from the training data and secondly, the classification is done by the use of the adaptive super pixel algorithm and then the test data is analyzed. Adaptive super pixel algorithm can adjust the weights of various features based on their discriminating ability. After the application of algorithm, the diabetic eye is detected by means of various parameters like colour, texture, spatial distance, contour, mean, standard deviation, entropy and maximum pixel points. This research can aid the doctor for easy detection of the disease as it given an accuracy of about 98.33%.

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Section: Literature Reviewmentioning

confidence: 94%

Diabetic Retinopathy Feature Extraction and Classification using Adaptive Super Pixel Algorithm

Subramanian¹,

Saravanan²,

Nayak³

et al. 2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…In [22], the authors proposed a comparison between the Naïve Bayes and SVM to classify DR. Although the Naïve Bayes achieved an accuracy of 83.4% against 64.9% obtained by the SVM, the dataset used was composed of only 300 images.…”

Section: B Diabetic Retinopathymentioning

confidence: 99%

Deep Transfer Learning Strategy to Diagnose Eye-Related Conditions and Diseases: An Approach Based on Low-Quality Fundus Images

2023

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Data from the World Health Organization indicate that billion cases of visual impairment could be avoided, mainly with regular examinations. However, the absence of specialists in basic health units has resulted in a lack of accurate diagnosis of systemic or asymptomatic eye diseases, increasing the cases of blindness. In this context, the present paper proposes an ensemble of convolutional neural networks, which were submitted to a transfer learning process by using 38,727 high-quality fundus images. Next, the ensemble was tested with 13,000 low-quality fundus images acquired by low-cost equipment. Thus, the proposed approach contributes to advance the state-of-the-art with a novel deep transfer learning strategy, which is more suitable and feasible to be applied by emerging and under-developing countries. From lowquality images, the proposed approach was able to reach accuracies of 87.4%, 90.8%, 87.5%, 79.1% to classify cataract, diabetic retinopathy, excavation and blood vessels, respectively.INDEX TERMS Convolutional neural network; deep learning; eye-related conditions; fundus images; transfer learning.

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“…The top 10 data mining algorithms include KNN [22], Support Vector Machine (SVM), Naive Bayes [23,24], and Logistic Regression [25] as some of the more well-known algorithms. To perform classification, SVM [26,27] maps data to a high-dimensional information space using kernel functions but gets rejected owing to its large training time. On the whole, existing algorithms include drawbacks like inaccurate findings, high complexity and long training periods.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection with PSO and Convolutional Neural Network with Long Short-Term Memory for Medical Application

ASWATHI,

KUMAR,

RAMAKRISHNAN

2024

Preprint

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The presence of cardiovascular disease is the main cause of fatalities and illnesses throughout. Predicting heart disease prognosis in medical data analytics is quite difficult. The spectacular amount of unstructured data produced by search has demonstrated that significant features are crucial in enhancing the effectiveness of machine learning models. In this study, the dataset of hospitalised patients' heart failure survivors is analysed. It is important to discover critical characteristics and effective deep-learning methodologies to increase the accuracy of survival prediction for cardiovascular patients. This research is approached by using K-Means segmentation for partitioning data. After segmentation, the highest-ranking features chosen by Particle Swarm Optimization (PSO) are used to train machine learning models. Finally, trained data of patients are classified using a hybrid technique combining Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). The classification attains an overall accuracy of 94.63%, thereby supporting experts to predict disease earlier. The examination of the proposed is accomplished through Python Software and the outcomes reveal that the hybrid classification technique performs better metrics when contrasted with state-of-art approaches.

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Prediction of Chances - Diabetic Retinopathy using Data Mining Classification Techniques

Cited by 18 publications

References 8 publications

Diabetic Retinopathy Feature Extraction and Classification using Adaptive Super Pixel Algorithm

Diabetic Retinopathy Feature Extraction and Classification using Adaptive Super Pixel Algorithm

Deep Transfer Learning Strategy to Diagnose Eye-Related Conditions and Diseases: An Approach Based on Low-Quality Fundus Images

Feature Selection with PSO and Convolutional Neural Network with Long Short-Term Memory for Medical Application

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