Variable Selection Via Penalized Likelihood Plays An Important Role Of Statistics And Machine Learning

Instead we use the original predictors to predict the response. The original dataset was split into a training set that consists of 75% of the total observations and a test set that consists of 25% of the total observations. Observations were chosen randomly. Supervised learning methods was conducted on the training set to obtain a model, then the model was used on the test set to assess the prediction performance. The values for “K” in KNN were tuned via cross-validation. Due to the volume of the data, the “cost” parameter in the SVM was chosen somewhat ad hoc and the “mtry” parameter in the random forest was chosen as default. The error rates are as

To begin with, the Lassi is a very beneficial tool when searching for ways to improve because before solving a problem, the first step is to know there is a problem also what the problem is. The lassi evaluates the questions these questions got me thinking in depth about what I should be doing and what I am actually doing. When completing the Lassi I could see where exactly I need to be improving. These problems are interfering with becoming a successful student in collage as well as in everyday activities. For instance, when I need to complete homework or test taking with being more prepared or used the time prior to these certain assignments or the test the grades earned will feel the impact what I do prior affects my grade possibly I got

The logistic regression models can be used to compare hospital profiles depends on risk- adjusted death with thirty days of non-cardiac surgery. It can efficiently manage the resource allocation by identifying the high risk areas and predicting the need and usage of various resources

Many hospitals are maintaining their patient’s database online like the records related to tests suggested, their results and the prescriptions suggested. This generates huge data which could be in any form like text, numbers as well as images and videos. In fact, all this data is important in making clinical decisions. In order to handle such a large data efficiently, usage of multistage classifier has become necessary. In existing systems, all the features are tested at a time by the classifier in order to detect whether patient is suffering from that particular disease or not. The entire testing consumes lot of time if the system is testing all attributes of a patient who is not actually suffering from the disease. So in such cases if we test the attributes step by step with few attributes in each step then we will be able to arrive at conclusion in primary stage itself leading to more efficient use of time as well as money. Simultaneously patient is also relieved from unnecessary stress as well as fatigue. This system optimizes resources very efficiently. Also it aims to identify the problem in the very preliminary stage and suggest reliable solution to problems increasing life of

Predictive modeling is another form of data analytics that is focused on forecasting the future medical costs. This model utilizes patients’ medical information in an effort to evaluate health risks and forecast the future of medical utilization (Ingenix, 2006). A large variety of predictive modeling procedures are available, which are created to assign a specific risk level or score to the patients (Asparouhov, 2012). Risk scores are controlled by the risk markers and are given to each patient in a specific population (Ingenix, 2006). In using any past diagnoses and other information gathered, the predictive models will calculate the individual patient costs that can then be used by healthcare providers and the insurers. Therefore, certain

A hierarchical Bayesian model is developed in the inverse problem setup. The Bayesian approach contains a natural mechanism for regularization in the form of a prior distribution, and a LASSO type prior distribution is used to strongly induce sparseness. We propose a variational type algorithm by minimizing the Kullback–Leibler divergence between the true posterior distribution and a separable approximation. The proposed method is illustrated on several two-dimensional linear and nonlinear inverse problems, e.g., Cauchy problem and permeability estimation problem. The proposed method performs comparably with full Markov chain Monte Carlo (MCMC) in terms of accuracy and is computationally

going for algorithms like MFCC ,GMM is the fact that they are very popular and

Cross-validation method was used to evaluate the predictive power of models. Dataset was equally divided into two parts. Models were fitted on training set and tested on testing set.

In the next paragraphs, this thesis paper discussed various factors of 3 mentioned MDSS like main motivation for the implementation of new MDSS, different data mining (DM) algorithms used, techniques used to improve the

LAD is another statistical optimality criterion and the statistical optimization technique that relies on it. Similar to other celebrity-like optimization techniques, LAD aims to find a function which closely approximate our data.

2. Identify the criteria – This step sometimes requires more than on objective. This step could easily solve other problems. If a criteria is missed, it could change the outcome of the results. However, when all criteria has been identified, it optimizes the results.

Multivariate regression is a statistical tool used to predict the functional relationship between some dependent variable and a set of independent variables [13, 14]. It comes as a generalization to simple univariate regression models therefore it will be introduced accordingly. However selecting which variables best influence the survival rate in LC is quite difficult. Out of 153 collected prognostic variables, only few have a significant impact on the 5-year survival rate while the rest have almost no effect other than complicating the model. Thus before implementing a neural network predictor we need to sift through the variables to build a meaningful model and to that end several mathematical tools were used including correlation analysis.

Bayesian regression models are of great values to genomic prediction but computing of these models can be very intensive [14]. This is becoming the bottleneck in practical genomic selection programs, in which the data sizes are often large. Thus, parallel programming and computing strategies are needed in order to increase the computational efficiency and generate high computational throughputs for genomic selection. For a Bayesian regression model implanted via multiple-chain MCMC, the speed-upspeedup of computing heavily depends on the length of burn-in which cannot be parallelized. Often, the length of burn-in is set up arbitrarily, which can be too short or too long. In the former case, the Markov chains are not converged and generated

The next step in the sentiment analysis is to extract and select text features. Here feature selection technique treat the documents as group of words (Bag of Words (BOWs)) which ignores the position of the word in the document.Here feature selection method used is Chi-square (x2).

In medical fields, clinical databases are recently becoming increasingly large so that more experts are needed to deal the data. Since the majority of the medical data sets are non-linear, it is difficult for people to analysis and classify a large amount of them. Therefore if there are some specific algorithms which could automatically handle numerous data, it could efficiently reduce the human