KETEPATAN KLASIFIKASI STATUS DIABETES MELITUS DENGAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE

Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...

Multivariate Adaptive Regression Splines

Predicting Flow Number of Asphalt Mixtures Based on the Marshall Mix design Parameters Using Multivariate Adaptive Regression Spline (MARS)

Rutting is one of the major distresses in the flexible pavements, which is heavily influenced by the asphalt mixtures properties at high temperatures. There are several methods for the characterization of the rutting resistance of asphalt mixtures. Flow number is one of the most important parameters that can be used for the evaluation of rutting. The flow number is measured by the dynamic creep...

Diabetes Disease Diagnosis Using Multivariate Adaptive Regression Splines

Diabetes mellitus is a chronic disease, also associated with an increased risk for heart disease and is emerging as a serious health challenge in India. It requires continuing medical care and patient self-management education to prevent severe problems and to reduce the risk of long-term problems. Healthcare industry stores a large amount of data which is not properly used to discover the hidd...

Discussion: Multivariate Adaptive Regression Splines

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