The Design of Diabetic Retinopathy Classifier Based on Parameter Optimization SVM

Han, Jiangxue; Jiang, Wenping; Dai, Cuixia

doi:10.1109/iciibms.2018.8549947

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Another research [21] studied that the use of SVM for credit risk classification is interesting to do because SVM is characterized by versatility, resilience, and computational simplicity. At the same time, even in the case of a limited number of samples, SVM can obtain better classification results.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Improved Support Vector Machine Performance Using Particle Swarm Optimization in Credit Risk Classification

Manik

Nababan

Tulus

2022

J. Tek. Inform. (JUTIF)

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In Classification using Support Vector Machine (SVM), each kernel has parameters that affect the classification accuracy results. This study examines the improvement of SVM performance by selecting parameters using Particle Swarm Optimization (PSO) on credit risk classification, the results of which are compared with SVM with random parameter selection. The classification performance is evaluated by applying the SVM classification to the Credit German benchmark credit data set and the private credit data set which is a credit data set issued from a local bank in North Sumatra. Although it requires a longer execution time to achieve optimal accuracy values, the SVM+PSO combination is quite effective and more systematic than trial and error techniques in finding SVM parameter values, so as to produce better accuracy. In general, the test results show that the RBF kernel is able to produce higher accuracy and f1-scores than linear and polynomial kernels. SVM classification with optimization using PSO can produce better accuracy than classification using SVM without optimization, namely the determination of parameters randomly. Credit data classification accuracy increased to 92.31%.

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Section: Review Of Related Studiesmentioning

confidence: 99%

Improved Support Vector Machine Performance Using Particle Swarm Optimization in Credit Risk Classification

Manik

Nababan

Tulus

2022

J. Tek. Inform. (JUTIF)

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“…Deep learning prediction models developed from neural network algorithms have been gaining a lot of interest following their successful implementation in image recognition and natural language processing applications (He et al, 2016; Young et al, 2018). In genomics, deep learning applications are helping to identify functional DNA sequences, protein binding motifs and epigenetic marks (Alipanahi et al, 2015; Zhou and Troyanskaya, 2015; Zhang et al, 2018).…”

Section: Supervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning SNP Based Prediction for Precision Medicine

et al. 2019

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In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.

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“…This model predicts the stage of glaucoma which was defined as PDR, mellow, moderate, and serious class. Han et al [43] proposed a support vector machine (SVM) parameter optimization algorithm for classifying the DR. Then K-fold cross-validation (K-CV) with genetic algorithm and grid search were further applied for parameter optimization. The proposed model achieved a classification accuracy of 98.33% in 31.13 seconds.…”

Section: Technique Based Analysis Of Ophthalmologymentioning

confidence: 99%

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Kumar¹,

Kumar²,

Gupta³

2018

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment's it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less. OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc. METHODS: AI and Machine Learning Technique are used to conduct this research for analysis. RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology. CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.

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The Design of Diabetic Retinopathy Classifier Based on Parameter Optimization SVM

Cited by 6 publications

References 8 publications

Improved Support Vector Machine Performance Using Particle Swarm Optimization in Credit Risk Classification

Improved Support Vector Machine Performance Using Particle Swarm Optimization in Credit Risk Classification

Machine Learning SNP Based Prediction for Precision Medicine

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

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