What is the added value of using non-linear models to explore complex healthcare datasets?

Health care is a complex system and it is therefore expected to behave in a non-linear manner. It is important for the delivery of health interventions to patients that the best possible analysis of available data is undertaken. Many of the conventional models used for health care data are linear. This research compares the performance of linear models with non-linear models for two health care data sets of complex interventions. Logistic regression, latent class analysis and a classification artificial neural network were each used to model outcomes for patients using data from a randomised controlled trial of a cognitive behavioural complex intervention for non-specific low back pain. A Cox proportional hazards model and an artificial neural network were used to model survival and the hazards for different sub-groups of patients using an observational study of a cardiovascular rehabilitation complex intervention. The artificial neural network and an ordinary logistic regression were more accurate in classifying patient recovery from back pain than a logistic regression on latent class membership. The most sensitive models were the artificial neural network and the latent class logistic regression. The best overall performance was the artificial neural network, providing both sensitivity and accuracy. Survival was modelled equally well by the Cox model and the artificial neural network, when compared to the empirical Kaplan-Meier survival curve. Long term survival for the cardiovascular patients was strongly associated with secondary prevention medications, and fitness was also important. Moreover, improvement in fitness during the rehabilitation period to a fairly modest 'high fitness' category was as advantageous for long-term survival as having achieved that same level of fitness by the beginning of the rehabilitation period. Having adjusted for fitness, BMI was not a predictor of long term survival after a cardiac event or procedure. The Cox proportional hazards model was constrained by its assumptions to produce hazard trajectories proportional to the baseline hazard. The artificial neural network model produced hazard trajectories that vary, giving rise to hypotheses about how the predictors of survival interact in their influence on the hazard. The artificial neural network, an exemplar non-linear model, has been shown to match or exceed the capability of conventional models in the analysis of complex health care data sets

Application of statistical and neural network model for oil palm yield study

This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural network’s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yiel

Predicting Bitcoin Returns Using Artificial Neural Networks - An Application of Large Datasets to Convolutional Neural Networks and Long Short-Term Memory Based Artificial Neural Networks in Finance.

Time series forecasting is one of the foremost challenges studied in finance. In this thesis various Convolutional Neural Network and Long Short Term Memory Artificial Neural Network models are used to predict Bitcoin returns. Previous literature has explored using data from Sentiment analysis of Social Media, and Blockchain information in isolation. This thesis seeks to combine the predictive power of earlier smaller models into a larger model that better utilizes a broader category of features in time series prediction. The resulting models are able to predict Bitcoin returns well, beating out simpler methods that do not utilize Artificial Neural Networks

An Analysis of Applying Artificial Neural Networks for Employee Selection

This paper describes the research and development of an artificial neural network system as a decision aid for employee selection. The ability of the artificial neural network to recognize patterns even using noisy data for employee selection and performance evaluation suggests this framework has significant potential advantage over traditional statistical models, such as regression analysis. Further, the neural model eliminates several methodological problems associated with the use of multiple regression, including non- linearity, incorrect function form specification, and heteroskedasticity

Comparison of Angstrom-Prescott, Multiple Regression and Artificial Neural Network Models for the Estimation of Global Solar Radiation in Warri, Nigeria

In this paper, the application of artificial neural network, Angstrom-Prescott and multiple regressions models to study the estimation of global solar radiation in Warri, Nigeria for a time period of seventeen years were carried out. Our study based on Multi-Layer Perceptron (MLP) of artificial neural network was trained and tested using seventeen years (1991-2007) meteorological data. The error results and statistical analysis shows that MLP network has the minimum forecasting error and can be considered as a better model to estimate global solar radiation in Warri compare to the estimation from multiple regressions and Angstrom-Prescott models

Comparison of artificial neural network, logistic regression and discriminant analysis methods in prediction of metabolic syndrome.

Introduction: Artificial neural networks as a modern modeling method have received considerable attention in recent years. The models are used in prediction and classification in situations where classic statistical models have restricted application when some, or all of their assumptions are met. This study is aimed to compare the ability of neural network models to discriminant analysis and logistic regression models in predicting the metabolic syndrome. Materials & Methods: A total of 347 participants from the cohort of the Tehran Lipid and Glucose Study (TLGS) were studied. The subjects were free of metabolic syndrome at baseling according to the ATPIII criteria. Demographic characteristics, history of coronary artery disease, body mass index, waist, LDL, HDL, total cholesterol, triglycerides, fasting and 2 hours blood sugar, smoking, systolic and diastolic blood pressure were measured at baseline. Incidence of metabolic syndrome after about 3 years of follow up was considered a dependent variable. Logistic regression, discriminant analysis and neural network models were fitted to the data. The ability of the models in predicting metabolic syndrome was compared using ROC analysis and the Kappa statistic, for which, MATLAB software was used. Results: The areas under receiver operating characteristic (ROC) curve for logistic regression, discriminant analysis and artificial neural network models (15: 8: 1) and (15: 10: 10) were estimated as 0. 749, 0. 739, 0. 748 and 0. 890 respectively. Sensitivity of models were calculated as 0. 483, 0. 677, 0. 453 and 0. 863 and their specificity as 0. 857, 0. 660, 0. 910 and 0. 844 respectively. The Kappa statistics for these models were 0. 322, 0. 363, 0. 372 and 0. 712 respectively. Conclusion: Results of this study indicate that artificial neural network models perform better than classic statistical models in predicting the metabolic syndrome

Artificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield

A particular type of “Artificial neural network (ANN)â€, viz. Multilayered feedforward artificial neural network (MLFANN) has been described. To train such a network, two types of learning algorithms, namely Gradient descent algorithm (GDA) and Conjugate gradient descent algorithm (CGDA), have been discussed. The methodology has been illustrated by considering maize crop yield data as response variable and total human labour, farm power, fertilizer consumption, and pesticide consumption as predictors. The data have been taken from a recently concluded National Agricultural Technology Project of Division of Agricultural Economics, I.A.R.I., New Delhi. To train the neural network, relevant computer programs have been written in MATLAB software package using Neural network toolbox. It has been found that a three-layered MLFANN with (11,16) units in the two hidden layers performs best in terms of having minimum mean square errors (MSE) for training, validation, and test sets. Superiority of this MLFANN over multiple linear regression (MLR) analysis has also been demonstrated for the maize data considered in the study. It is hoped that, in future, research workers would start applying not only MLFANN but also some of the other more advanced ANN models, like ‘Radial basis function neural network’, and ‘Generalized regression neural network’ in their studies.Crop Production/Industries,