New health care article that uses quantitative research

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Search the GCU Library and find one new health care article that uses quantitative research. Do not use an article from a previous assignment, or that appears in the Topic Materials or textbook. Complete an article analysis and ethics evaluation of the research using the “Article Analysis and Evaluation of Research Ethics” template. See Chapter 5 of your textbook as needed, for assistance. While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Sample Solution

omized Hill Climbing
Perhaps the simplest of randomized optimization algorithms, Randomized Hill Climbing consists of a simple greedy hill climbing search with random restarts after each iteration. As such, with enough iterations, the algorithm will cover the entire search space without isolating itself to a single local minima. Due to the straightforward nature of the algorithm, there aren’t any hyperparameters to tune. We use a 70/30 train/test split in our experiment.
We applied the algorithm to calculate the weights of a neural network using multilayer perceptron hyperparameters listed in Table 1; a curve relating accuracy to iterations can be seen in Figure 1. Although the curve itself displays a clear upwards trend, there are several interesting phenomena to unpack. Firstly, the algorithm’s accuracy remains fixed to 44% for approximately the first 100 iterations; this is an indication that the algorithm’s search space was isolated to a positioning of weights that did not influence the accuracy of the classifier during this time. Furthermore, after the curve began to increase near iteration 100, there were still some large fluctuations – one of which decreased the model’s accuracy to 44%. These outliers likely involve cases where the algorithm’s search space allowed for modification of crucial network weights with greater effect on the model.

Figure 1. NN feed-forward accuracy over 1,000 iterations of the Randomized Hill Climbing algorithm.

Through 1,000 iterations, the algorithm was unable to reach the testing accuracy of 97.38% generated using backpropagation in Assignment 1; the testing curve plateaus just above 90%. Given exponentially mor

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