scholarly journals Machine Learning Approaches on Diabetic Retinopathy Prediction

2020 ◽  
pp. 341-345
Author(s):  
Gowri Prasad ◽  
Vrinda Raveendran ◽  
Vidya B M ◽  
Tejavati Hedge
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Comparative Study ◽  
Blood Sugar ◽  
Efficient Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Early Age ◽  
Learning Approaches

Diabetic retinopathy is a eye disorder which is developed due to high blood sugar that affects the neurons in retina. A dangerous fact about this disease is that it can lead to blindness. The possible cure is through detection of disease at early age. This can be done using different machine learning algorithms. This paper does a comparative study on different machine learning algorithms that can be used for early detection of diabetic retinopathy. This study is done to find out the most efficient algorithm suitable for the process and to increase the efficiency of the particular algorithm.

COMPARATIVE STUDY OF VARIOUS MACHINE LEARNING ALGORITHMS FOR TWEET CLASSIFICATION

2019 ◽  
Vol 6 (4) ◽  
pp. 12
Author(s):  
ABUBAKAR UMAR ◽  
A. BASHIR SULAIMON ◽  
BASHIR ABDULLAHI MUHAMMAD ◽  
S. ADEBAYO OLAWALE ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tweet Classification

scholarly journals Searching for improvements in predicting human eye colour from DNA

2021 ◽  
Author(s):  
Magdalena Kukla-Bartoszek ◽  
Paweł Teisseyre ◽  
Ewelina Pośpiech ◽  
Joanna Karłowska-Pik ◽  
Piotr Zieliński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sequencing Analysis ◽  
Learning Approaches ◽  
Human Eye ◽  
Software Analysis ◽  
Whole Exome ◽  
Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

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