Unit 5 Written Assessment
Uploaded by CaseyP on Feb 20, 2018
Unit 5 Written Assessment
Name
Institution Affiliation
Introduction
A formal approach is more suitable to validate the problem. The outlook offers a valuable tool that helps to reveal omissions, inconsistencies, and ambiguities in health records. Traditional approaches can't detect uncertainties and several other issues in health information system. Also, it enhances the efficacy and applicability of data in any health settings. However, a formal approach allows and complements exclusive validation procedures. Patient data efficiently used has excellent capability to improve and even save human lives. The need for the transparent information system is of great importance to creating a reliable service design and healthcare delivery system (Sammon, O'Connor & Leo, 2009).
The hospital collects demographic data of its patients. It includes analytical expression of patients' socioeconomic status such as education level, sex, marital status, religion, age, death rate, income rate, birth rate, family size, and occupation (Yanamadala et al, 2016). The data is stored in structured form. Structured information is accessible and readily searchable using straightforward algorithms. On the other hand, unstructured data is the opposite of being readily available.
According to Sun and Reddy (2013), data mining process uses several essential techniques that define data recovery and mining operation. Techniques such as prediction, classification, long-term processing, clustering, association, decision trees, sequential patterns, and combinations are applicable in both structured and unstructured data sets. For this assessment, classification and association techniques are suitable to extract the required patient data. Preferably, I will use the Naïve Bayes (NB) mining algorithm. It is a supervised algorithm that uses Bayes' Theorem to make predictions within a data set. By doing so, it facilitates the detection of the issue from the relevant evidence as presented in the patient data (Sun & Reddy, 2013). To arrive at an evidence-based answer, approximately 75 percent of the patient need to be successfully extracted.
In most cases, researchers undermine the data pre-processing step in data extraction exercise. “Garbage In, Garbage Out” is a significant phrase in data mining process. Methods used to gather the data need to be controlled to avoid ambiguities and irrelevant or redundant data combinations such as (Gender: Male, Pregnant: Yes). Also, to make sure that all essential values are available to avoid misleading conclusions. Therefore, quality of data and accurate representation is of importance before analysis. Data pre-processing improves the quality of the data by ensuring that the data isn't noisy, doesn't have inconsistencies, and missing values...