Tejas Wadiwala

Work place: Lakehead University, Department of Computer Science, Thunder Bay, Ontario, Canada

E-mail: twadiwal@lakeheadu.ca


Research Interests: Artificial Intelligence, Database Management System, Data Structures and Algorithms


Tejas Wadiwala, M.sc Computer Science Student at Lakehead University, ON, Canada. Date of birth – 30-5-1996, India. Research Interests include Business Management, Artificial Intelligence, Big Data. Previous experience at Accenture, India as an Application Developer. Contact – twadiwal@lakeheadu.ca.

Previous Publications in Journal – IJERT.

Author Articles
Big Data Analytics and Visualization for Hospital Recommendation using HCAHPS Standardized Patient Survey

By Ajinkya Kunjir Jugal Shah Navdeep Singh Tejas Wadiwala

DOI: https://doi.org/10.5815/ijitcs.2019.03.01, Pub. Date: 8 Mar. 2019

In Healthcare and Medical diagnosis, Patient Satisfaction surveys are a valuable information resource and if studied adequately can contribute significantly to recognize the performance of the hospitals and recommend it. The analysis of measurements concerning patient satisfaction can act as a valid indicator for giving recommendations to the patient about a specific hospital, as well as can provide insights to improve the services for healthcare organizations. The primary objective of the proposed research is to carry out an in-depth investigation of all the measurements in HCAHPS survey dataset and distinguish those that contribute considerably to the hospital suggestions. This work performs predictive analysis by building multiple classification models, each of which examined and evaluated to determine the efficiency in predicting the target variable, i.e., whether the hospital is recommended or not, based on specific set of measurements that contribute to it. All the models built as a part of research specified the same list of measure id is that help in deriving the target. It provides an insight into how caregiver interaction, emphasizes on the services rendered by the caregiver and overall patient experience makes a hospital highly valued and preferred. An in depth-analysis is conducted to derive the implementation results and have been stated in the later part of the paper.

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