The Italian Regional Dualism: A MARS and Panel Data Analysis

Prolonged and obstructed labor are the type of abnormal labor that may lead to maternal and fetal mortality. This study established the model for predicting prolonged and obstructed labor in East Nusa Tenggara. A health facilities-based case-control study was conducted in November 2017 among 570 women who gave birth at public health facilities in East Nusa Tenggara. Data were obtained by reviewing antenatal records, the mother's card, and partographs. In bivariate analysis, all variables with a p-value less than 0.25 determined by chi-square for categorical and independent t-test for numerical variables were included in multivariate analysis. Multivariate Adaptive Regression Splines (MARS) analysis was used to establish the final prediction model. The present study found that women <22, >26, and >34, with Hb levels of <12.5 gr%, and had nulliparity or multiparity (4 times) were reported as a higher risk of prolonged and obstructed labor. Meanwhile women with a fundal height of <34 cm, a height of >156 cm and >149 cm, a history of normal labor, presentation of the fetus behind the head, gestational weight gain of <12.3 kg, and pre-pregnancy BMI of <28.9 kg/m2 were identified as factors decreasing the risk of prolonged and obstructed labor. In conclusion, significant predictors of the outcome were maternal characteristics (age, parity, height, and history of labor method), maternal nutrition status (BMI pre-pregnancy, gestational weight gain, and hemoglobin levels), and fetal status (fundal height and fetal presentation).

Major specialization at High School is aimed to gives opened opportunity for students to choose subject that are interest and develop their potential in accordance with the abilities, interests, talents, and personality. Major specialization at High school is influenced by some factors. To detect those factors, used biner logistic regression method and Multivariate Adaptive Regression Spline (MARS). Biner Logistic Regression is method that describes relationship between dependent variable and some independent variable, with independent variable has been coded 1 as representing the presence of the characteristic, and 0 as representing the absence of the characteristic. MARS is multivariate nonparametric regression method that development of Recursive Partitioning Regression (RPR) method and Spline method for high dimensional data that produces accurate prediction and continuous models on knots. Both of the methods are compared to know the best method used in research. From the result of analysis using biner logistic regression method and MARS, concluded that major specialization has been influenced by mathematic score, science score and relationship between students and friends. From proportion test, concluded classification that formed by regression logistic is as good as by MARS. Keywords : Major specialization at High School, Biner Logistic Regression, Mutlivariate Adaptive Regression Spline (MARS), Clasification

Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete

Analyzing angle crashes at unsignalized intersections using machine learning techniques

Survival analysis is a statistical method where the outcome variable that is considered is the time until the occurrence of an event or called an event. Survival analysis in the medical world can be used to test the survival of patients suffering from a disease, one of which is kidney failure. Based on the results of the 2013 Basic Health Research, 0.2% of the total population of Indonesia, namely 499,800 people, experienced kidney failure. Kidney failure is a process of decreasing kidney function that requires renal replacement therapy, in the form of hemodialysis or kidney transplantation. If the survival data involve independent variables that are thought to affect the survival time, then the Cox Proportional Hazard (PH) regression approach can be developed which can be developed for the Multivariate Adaptive Regression Spline (MARS) approach. The MARS approach is a nonparametric regression approach with high-dimensional data, namely data with an independent variable of 3≤𝑝≤20 and a data sample of 50≤N≤1000. This study aims to describe the characteristics of patients with kidney failure, identify the factors that affect the survival time of patients with kidney failure and determine the MARS model. The MARS approach is obtained from a combination of functional basis, maximum interaction, and minimum observation by trial and error. Based on the model that has been formed using the MARS approach, it is found that the most influential variables on the survival time of patients with kidney failure are age, urea levels, and creatine levels.
 Key words: Renal failure, hemodialysis, survival analysis, nonparametric, MARS

Studies have shown that interactions of single nucleotide polymorphisms (SNPs) may play an important role in understanding the causes of complex disease. We have proposed an integrated machine learning method that combines two machine-learning methods-Random Forests (RF) and Multivariate Adaptive Regression Splines (MARS)-to identify a subset of important SNPs and detect interaction patterns more effectively and efficiently. In this two-stage RF-MARS (TRM) approach, RF is first applied to detect a predictive subset of SNPs, and then MARS is used to identify the interaction patterns. We evaluated the TRM performances in four models. RF variable selection was based on out-of-bag classification error rate (OOB) and variable important spectrum (IS). Our results support that RF(OOB) had better performance than MARS and RF(IS) in detecting important variables. This study demonstrates that TRM(OOB) , which is RF(OOB) plus MARS, has combined the strengths of RF and MARS in identifying SNP-SNP interactions in a scenario of 100 candidate SNPs. TRM(OOB) had greater true positive rate and lower false positive rate compared with MARS, particularly for searching interactions with a strong association with the outcome. Therefore, the use of TRM(OOB) is favored for exploring SNP-SNP interactions in a large-scale genetic variation study.

This article investigates the feasibility of multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for the prediction of over consolidation ratio (OCR) of clay deposits based on Piezocone Penetration Tests (PCPT) data. MARS uses piece-wise linear segments to describe the non-linear relationships between input and output variables. LSSVM is firmly based on the theory of statistical learning, and uses regression technique. The input parameters of the models are corrected cone resistance (q t ), vertical total stress (σv), hydrostatic pore pressure (u 0), pore pressure at the cone tip (u 1), and the pore pressure just above the cone base (u 2). The developed LSSVM model gives error bar of predicted OCR. Equations have also been developed for prediction of OCR. The performance of MARS and LSSVM models has been compared with the traditional methods for OCR prediction. As the results reveal, the proposed MARS and LSSVM models are robust models for determination of OCR.

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas

In this article, a new algorithm to select the relevant features is proposed for handling microarray data with the specific aim of increasing classification accuracy. In particular, the optimal genes are extracted using filter and wrapper feature selection algorithms. Here, the use of non-parametric regression algorithm called Multivariate Adaptive Regression Spline (MARS) followed by proposed Random Forest Statistical Test (RFST) algorithm are being studied. The study evaluates the comparative performance of the results of RFST and MARS with existing algorithms on ten standard microarray datasets. For performance analysis, three parameters are taken into consideration, namely, the number of selected features, runtime, and classification accuracy. Experimental results indicate that different feature selection algorithms yield different candidate gene subset; therefore, a Hybrid approach is applied to determine the best candidate genes to provide maximum information about the disease. The findings foretell that the RFST is performing better on six out of ten datasets whereas MARS is performing better on other datasets.

The paper presents the application of a new statistical technique, multivariate adaptive regression splines (MARS), to a flexible pavement roughness prediction model. MARS is a non-parametric function estimation technique that shows great promise for fitting non-linear multivariate functions. The MARS approach was used to develop a roughness equation, based on available input, and was able to identify the threshold values of each input and the most important variables contributing to the roughness equation. The MARS technique allows easy interpretation of the relative importance of pavement condition variables, environmental factors and traffic for the overall fit.

A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such as unrealistic time frames and lack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of stand-alone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models.

This chapter provides a description of how to use PROC ADAPTIVEREG for generating multivariate adaptive regression splines (MARS) models for univariate continuous and dichotomous outcomes as well as how to evaluate and compare MARS models with likelihood cross-validation (LCV) scores. Comparison of MARS models to adaptive fractional polynomial models on the basis of LCV scores is also covered as well as how to adaptively transform MARS models. Example code is provided for generating models for predicting the univariate continuous outcome death rate per 100,000 in terms of available predictors as also addressed in Chaps 2, 3, 16, and 17 as well as models for predicting the univariate dichotomous outcome a high mercury level in fish over 1.0 ppm versus a lower level in terms of available predictors as also addressed in Chaps 8, 9, 16 and 17.

This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for determination of <TEX>$f_s$</TEX> of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of <TEX>$f_s$</TEX> of driven piles in clay.

Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies in its ability to estimate contributions of some basis functions so that both additive and interactive effects of the predictors are allowed to determine the response variable. The MARS method consists of two parts: forward and backward algorithms. Through these algorithms, it seeks to achieve two objectives: a good fit to the data, but a simple model. In this article, we use a penalized residual sum of squares for MARS as a Tikhonov regularization problem, and treat this with continuous optimization technique, in particular, the framework of conic quadratic programming. We call this new approach to MARS as CMARS, and consider it as becoming an important complementary and model-based alternative to the backward stepwise algorithm. The performance of CMARS is also evaluated using different data sets with different features, and the results are discussed.

Establishing appropriate heatwave thresholds is important in reducing adverse human health consequences as it enables a more effective heatwave warning system and response plan. This paper defined such thresholds by focusing on the non-linear relationship between heatwave outcomes and meteorological variables as part of an inductive approach. Daily data on emergency department visitors who were diagnosed with heat illnesses and information on 19 meteorological variables were obtained for the years 2011 to 2016 from relevant government agencies. A Multivariate Adaptive Regression Splines (MARS) analysis was performed to explore points (referred to as “knots”) where the behaviour of the variables rapidly changed. For all emergency department visitors, two thresholds (a maximum daily temperature ≥ 32.58°C for 2 consecutive days and a heat index ≥ 79.64) were selected based on the dramatic rise of morbidity at these points. Nonetheless, visitors, who included children and outside workers diagnosed in the early summer season, were reported as being sensitive to heatwaves at lower thresholds. The average daytime temperature (from noon to 6 PM) was determined to represent an alternative threshold for heatwaves. The findings have implications for exploring complex heatwave-morbidity relationships and for developing appropriate intervention strategies to prevent and mitigate the health impact of heatwaves.