Predicting Diabetes Risk Using an Improved Apriori Algorithm

The data mining is able to analyze data and recover the valuable insights from the data. These insights are used for serving in different applications. In this context different data mining algorithms has been developed among them frequent pattern mining has an essential role. In this paper, the frequent pattern mining technique has been implemented for analyzing the diabetic risk. In this context, the popular diabetic dataset has been obtained. Then, the preprocessing has been done on dataset for cleaning the dataset. Next, an encoding process has been developed to transform the dataset. This transform dataset is an effort to deal with the continuous values using the frequent pattern algorithm. Further a modified apriori algorithm has been employed to understand and establish the relationships between diabetic attributes. The experiments have been carried out and theexperimental performance of the improved apriori algorithms has been measured. Additionally a comparison has also been performed with three popular frequent pattern mining algorithms. According, to the performance, we found that the proposed apriori algorithm is efficient and accurate algorithm to predict the diabetic risk

Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of Ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results

Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results

A Machine Learning Technique for Detection of Social Media Fake News

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Análisis de imágenes de la retina del ojo para determinar el nivel de retinopatía diabética utilizando técnicas de inteligencia artificial

Analizar imágenes de retinografías para incrementar el grado de identificación del nivel de retinopatía diabética en pacientes que padecen diabetes utilizando técnicas de inteligencia artificial.El propósito principal de este estudio de investigación es enfocarse en utilizar técnicas de inteligencia artificial para incrementar la identificación del nivel de retinopatía diabética en pacientes que padecen diabetes mediante el análisis de imágenes de retinografías. Se desarrolló una aplicación web para la Asociación Ecuatoriana de Diabetes ubicada en Quito, Ecuador, y se utilizó la técnica de redes neuronales convolucionales en el entrenamiento de un modelo de aprendizaje automático con el objetivo de detectar automáticamente retinopatía diabética. El aplicativo web se conecta con un módulo de predicción entrenado con cinco tipos de arquitecturas de redes neuronales, y se determinó que la red neuronal ResNet152V2 tiene la mayor tasa de detección con un 80% de precisión. Se empleó la prueba estadística de Chi Cuadrado y la correlación de Spearman para llevar a cabo la validación correspondiente, y se observó que la aplicación superó al análisis visual de los especialistas con un margen de error del 19.9%, mientras que los especialistas obtuvieron un margen de error del 35.5%. En términos generales, se puede concluir que los resultados obtenidos indican que la aplicación ha logrado satisfactoriamente su objetivo principal, que es mejorar la detección del nivel de retinopatía diabética en pacientes con diabetes.Ingenierí

Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy

Diabetes Retinopathy is a disease which results from a prolonged case of diabetes mellitus and it is the most common cause of loss of vision in man. Data mining algorithms are used in medical and computer fields to find effective ways of forecasting a particular disease. This research was aimed at determining the effect of using feature selection in predicting Diabetes Retinopathy. The dataset used for this study was gotten from diabetes retinopathy Debrecen dataset from the University of California in a form suitable for mining. Feature selection was executed on diabetes retinopathy data then the Imple�mentation of k-Nearest Neighbour, C4.5 decision tree, Multi-layer Perceptron (MLP) and Support Vector Machines was conducted on diabetes retinopathy data with and without feature selection. There was access to the algorithms in terms of accuracy and sensitivity. It is observed from the results that, making use of feature selection on algorithms increases the accuracy as well as the sensitivity of the algorithms considered and it is mostly reflected in the support vector machine algorithm. Making use of feature selection for classification also increases the time taken for the prediction of diabetes retinopathy