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Age-Related Macular Degeneration (AMD) is a dangerous, chronic, and progressive illness that mostly affects people over 60 years old. This disease is related to the appearance of drusen: deposits of extracellular material located in the macular region. One way to effectively and non-invasively pre-diagnose AMD is by detecting the presence of drusen in fundus images. In this work we propose a new method that combines Digital Image Processing, Mathematical Morphology and a robust and powerful Machine Learning model: a Support Vector Machine (SVM). The enclosed macular region is subjected to a contrast enhancement method, followed by the application of basic morphological operations. We use invariant moments as the features of the processed image. The resulting vector is classified by an SVM as positive or negative for drusen. The proposed method is able to discriminate between healthy and afflicted cases with a classification accuracy that outperforms many well-regarded state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper the Naïve Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed. [ABSTRACT FROM AUTHOR]