BackgroundLASIK is the use of excimer lasers to treat therapeutic and refractive visual disorders, ranging from superficial scars to nearsightedness (myopia), and from astigmatism to farsightedness (hyperopia). The purposes of this study are to checking the applicability and psychometric properties of the SERVQUAL on Lasik surgery population. Second, use SEM methods to investigate the loyalty, perceptions and expectations relationship on LASIK surgery.MethodsThe method with which this study was conducted was questionnaire development. A total of 463 consecutive patients, attending LASIK surgery affiliated with Chung Shan Medical University Eye Center, enrolled in this study. All participants were asked to complete revised SERVQUAL questionnaires. Student t test, correlation test, and ANOVA and factor analyses were used to identify the characters and factors of service quality. Paired t test were used to test the gap between expectation and perception scores and structural equation modeling was used to examine relationships among satisfaction components.ResultsThe effective response rate was 97.3%. Validity was verified by several methods and internal reliability Cronbach's alpha was > 0.958. The results from patient's scores were very high with an overall score of 6.41(0.66), expectations at 6.68(0.47), and perceptions at 6.51(0.57). The gap between expectations and perceptions was significant, however, (t = 6.08). Furthermore, there were significant differences in the expectation scores among the different jobs. Also, the results showed that the higher the education of the patient, the lower their perception score (r = -0.10). The factor loading results of factor analysis showed 5 factors of the 22 items of the SERVQUAL model. The 5 factors of perception explained 72.94% of the total variance there; and on expectations it explained 77.12% of the total variance of satisfaction scores.The goodness-of-fit summary, of structure equation modeling, showed trends in concept on expectations, perceptions, and loyalty.ConclusionThe results of this research appear to show that the SERVQUAL instrument is a useful measurement tool in assessing and monitoring service quality in LASIK service, and enabling staff to identify where improvements are needed, from the patients' perspective. There were service quality gaps in the reliability, assurance, and empathy. This study suggested that physicians should increase their discussions with patients; which has, of course, already been proven to be an effective way to increase patient's satisfaction with medical care, regardless of the procedure received.

Missing data are common in industrial sensor readings owing to system updates and unequal radio-frequency periods. Existing methods addressing missing data through imputation may not always be appropriate. This study presented a sorted missing percentages technique for filtering attributes when building machine learning classification models using sensor readings with missing data. Signal detection theory was employed to evaluate the distinguishing ability of resulting models. To evaluate its performance, the proposed technique was applied to a publicly available air pressure system dataset, which then was used to build several classifiers. The experimental results indicated that the proposed technique allowed a logistic regression model to achieve the best accuracy score (99.56%) and a better distinguishing ability (response bias of 0.0013, adjusted response bias of 0.0044, and decision criterion of −1.8994) compared with the methods applied to the same dataset and reported in papers published between 2016 and 2019 March on binary classification, wherein attributes with more than 20% of missing data were filtered out. The proposed technique is suitable for industrial sensor data analysis and can be applied to the scenarios dealing with missing data owing to unequal radio-frequency periods or a system being updated with new fields.

The problem of missing data is frequently met in time series analysis. If not appropriately addressed, it usually leads to failed modeling and distorted forecasting. To deal with high market uncertainty, companies need a reliable and sustainable forecasting mechanism. In this article, two propositions are presented: (1) a dedicated time series forecasting scheme, which is both accurate and sustainable, and (2) a practical observation of the data background to deal with the problem of missing data and to effectively formulate correction strategies after predictions. In the empirical study, actual tray sales data and a comparison of different models that combine missing data processing methods and forecasters are employed. The results show that a specific product needs to be represented by a dedicated model. For example, regardless of whether the last fiscal year was a growth or recession year, the results suggest that the missing data for products with a high market share should be handled by the zero-filling method, whereas the mean imputation method should be for the average market share products. Finally, the gap between forecast and actual demand is bridged by employing a validation set, and it is further used for formulating correction strategies regarding production volumes.