Linear Regression Model Based On Recursive Feature Elimination Technique

Since there is no one accepted operational definition of frailty, it is impossible to enumerate frail persons in the population. Therefore, prevalence of frailty varies widely depending on its definitions and patient selection. One European

The variable age is the independent variable and is a ratio level of measurement (Loiselle et al., 2011). The measure of central tendency to describe age are in table 1.2 are the mean of 57.62 which is the average age, the median which is the middle score within the distribution when all scores are organized of 58.5 and the mode of 58 which is the most frequently occurring age (Loiselle et al., 2011). The measures of variability are the range of 69 with a minimum age of 22 and a maximum age of 91, standard deviation which is the average deviation from the sample mean which is a value of 16.26, and the sample variance which is the standard deviation square and the value is 263.46 (Salkind, 2013). The distribution for this sample is described as a negative skew and the value obtained from table 1.2 is -0.22511(Salkind, 2013). A negative skew occurs when the median and the mode value are larger than the mean, within this sample the median is 58.5 the mode is 58 which is greater than the mean of 57.62, the tail would be pointed toward the left (Salkind, 2013). The kurtosis value is -0.65102 and this describes how peak or flat the curve is from the normal distribution curve which is described as mesokurtic (Salkind, 2013). The kurtosis has a large negative value which is representative of a flatter curve also know as playkurtic (Salkind, 2013).

Frailty is a common clinical syndrome in older persons that carries an increased risk of poor health outcomes. Frailty has been described as “a physiologic state of increased vulnerability to stressors that decrease physiologic reservoirs, resulting from aging and decrease function of multiple physiologic organs (1). The overall prevalence of frailty in USA community dwelling aged 65 or older in the United States ranges 7-12%. The prevalence of frailty increased with age from 3.9% in the 65-74 age group to 25% in the 85+group, and was greater in women than men (8% vs. 5%) (2).

As the age increase, life table factor decreases. It showed for the first 20 years was constantly decreasing and it became more stable throughout the next 20 years. This was best characterize as a type III survivorship curve.

It was reported that Swe M-EMS (EMS), which is “a functional test developed by Smith to measure mobility and functional status in frail elderly people,” can be a predictor of discharge destination in patients with a femoral neck fracture living in the community. However, the EMS should be used in combination with other factors, including age, living social or unsocial environment, and pre-fracture activity level in order to predict the discharge destination in patients. The EMS takes approximately 10 – 15 min to administer and requires a bed, a chair, walking aids if necessary, a measuring tape and a watch. The instrument consists of seven items with a total score of 20, where 20 represent independent mobility and zero totally dependent. The items are lying to sitting, sitting to lying, sitting to standing, standing balance, gait, timed walk and modified Functional Reach (FR). The EMS is valid, reliable and sensitive to change in elderly patients with medical or mobility problems. The physical therapist, who I observed with the patient left hip fracture, performed some tests mentioned in the articles and then she made the short and long term goals based on the test

The Fugl-Myer assessment had a good inter-rater reliability. Its ICCs ranged from .93 to 1.000. The test-retest reliability with an interval of two weeks, the ICCs were .834 to .972 which was also good.

Each L. A. C. E. score linked to a patient can be easily identified if specific rules are set in place. This is readily represented by using the IF -THEN statements.

Frailty assessment tools have been developed for the estimation of perioperative risks in this particular population. Whereas many tools have emerged both clinical and research settings, there is no consensus for which tool is a good measure. Musculoskeletal and Orthopedic surgical field has been facing frailty problems as other areas. Early detection of frailty is essential for managing and alleviating postoperative complications. Thus, the need of measuring frailty, as a tool for preoperative risk stratification and as a strategy to identify potential factors that can be adjusted to pave the way for better postoperative outcomes, is necessary. Therefore, this scoped review aims to identify frailty assessment tools and the extent of the research

According to the complexity of frailty, selecting frailty instrument to capture frail status accurately is a crucial way for providing better care in orthopedic. Thus, evaluating frailty measurement instruments applying in the clinical setting is essential to health care personnel. Although the cause of frailty remains unclear, choosing reliable and strong prediction is required especially in frail elderly with MSDs population. As we found the 8 frailty instruments currently used in orthopedic; the structural and components, clinical health outcomes, along with evaluation instrument psychometric properties of those frailty instruments will be described as

questionnaire, and Collin’s (2009) Graph of Life tool, as I know that it is important to

Frailty is characterized based on the five domains of Fried’s Frailty Index. The MFC, the authors, reported that they modified some components for the best suitable to their population. Kua and colleagues (2016) indicated that they adjusted the slowness component from SHARE frailty instrument because it was unsuitable for hip fracture patients to have their gait speed taken. For interpreting the MFC scores; the score of 0 were considered non-frail and scored 1–5 were considered as frail. The other study from Kistler and colleagues (2015) stated that they modified the exhaustion domains by using self-reported regarding effort and energy during walking time since the original of 15 –foot walk times was inappropriate to measure exhaustion for patients having hip surgery. The MFC scores; scores of 3 or higher were considered as frailty.

With the positive coefficients, we will see an increase in one unit of each variable separately compared with the advancement in diabetes. With a 0.05 parameter, the linear regression model selects 5 predictor variables with significance, age, tc, ldl, tch, and glu. To validate the assumption, we can plot the residuals versus the fitted values to see if there are any indications of signs of random distributions. For the residual plot, we see there are no indications or violations of random distribution and can calculate the MSE of the model, which is 3111.265. Next, we will leverage the best subset method to select the predictor variables that are truly impactful to the model.

In this case, level of significance, α was not provided. Therefore, the analysis will be evaluated based on two α values which are:

For the second week, December 10 – 16, the important variables were CY48, VaccinationRate, AverIncome, WinterAfterHumidity. The best model was also a decision tree. In this week, the training misclassification rate was 0.3158 and the validation miscalculation rate was 0.4651. The difference in the rates is noticeable and I would declare this model as useless. The model fell apart during the validation process and I would not trust it prediction future events.

Analysis procedure used a 2-stage approach to analyze and validate the data. formal reliability test was conducted with results from 2 different researchers followed by a classification scheme with reliable problem frequencies.