Factor Convergence and Predictive Analysis of the Five Factor and Alternative Five Factor Personality Models With the Five-Factor Personality Inventory for ICD-11 (FFiCD)

The authors analyze and compare the factor convergence and predictive power of the Revised NEO Personality Inventory (NEO-PI-R) and the Zuckerman-Kuhlman-Aluja Personality Questionnaire (ZKA-PQ/SF) with respect to the Five-Factor Personality Inventory for ICD-11 (FFiCD). A total of 803 White Spanish subjects were analyzed. All the personality domains had significant predictive power with regard to the FFiCD except NEO Openness. The explained variance of the personality domains with respect to FFiCD Negative Affectivity (71% and 77%) and Detachment (56% and 56%) were similar for NEO-PI-R and ZKA-PQ/SF, respectively, but the NEO-PI-R accounted for greater variance for FFiCD Anankastia, Dissociality, and Disinhibition. The FFiCD facets of Rashness, Thrill-Seeking (Disinhibition), and Unassertiveness (Detachment) were located in factors other than those theoretically expected. The authors conclude that normal personality measured by the NEO-PI-R and the ZKA-PQ/SF contribute, in a differential but complementary way, to knowledge of the maladaptive personality measured by the FFiCD.

Healthcare Data Analytics using Power BI

Innovations in computer technologies have revolutionized attention in recent years. Data analytics has emerged as a promising tool for determination problems in various health care connected disciplines. The effective utilization of knowledge mining in deeply noticeable fields like e-business, promoting and retail has prompted application in completely different businesses and divisions. Among these components merely finding is the medical services. Medical services organizations can reduce down on medical services expense and furnish better consideration with the help of predictive analysis. Enormous information likewise helps in diminishing medicine mistakes by improving budgetary and regulatory execution, and decrease readmission. The paper aims at systematic collection of patient-related healthcare data ,analyse through Microsoft Power BI after some transformations of data and determine major disciplines to improve the patient engagement, health system management, diagnosis and cost reduction.

Predictive Analysis in Business Analytics: Application of Decision Tree in Business Decision Making

Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on hand data with the final objective of improving performance of the corporation. Predictive analysis has been gaining much attention in the late 20th century and it has been around for decades, but as technology advances, so does this technique and the techniques include data mining, big data analytics, and prescriptive analytics. Last but not least, the decision tree methodology (DT) which is a supervised simple classification tool for predictive analysis which be fully scrutinized below for applying predictive business analytics and DT in business applications