Abstract

Abstract

Abstract
The agribusiness volatility is related to the uncertainty of the environment, rising demand, falling prices and new technologies. However, generation of agriculture data has increased over past years and can be used for a growing number of applications of data mining techniques in agriculture. The multidisciplinary approach of integrating computer science with agriculture will support the necessary decisions to be taken in order to mitigate risks and maximize profits. The present study analyzes different methods of regression applied in the study case of grapes production forecast. The selected methods were multivariate linear regression, regression trees, lasso and random forest. Their performance were compared against the predictions obtained by the company through the mean squared error and the coefficient of variation. The four regression methods used obtained better predictive results than the method used by the company with statistical significance < 0.5%. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract

Abstract
This work describes a problem oriented approach to analyze and predict the Age of Onset of Patients diagnosed with Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). We constructed, from a set of clinical and familial records, three sets of features which represent different characteristics of a patient, before becoming symptomatic. Using those features, we tested a set of machine learning regression methods, namely Decision Tree (Regression Tree), Elastic Net, Lasso, Linear Regression, Random Forest Regressor, Ridge Regression and Support Vector Machine Regressor (SVM). Later, we defined a baseline model that represents the current medical practice to serve as a guideline for us to measure the accuracy of our approach. Our results show a significant improvement of machine learning methods when compared with the current baseline. © 2018 IEEE.