An Intelligent Application for Determine Patient Satisfaction using Data Mining Techniques - Kayalvizhi, S.; Gunasekar, T.; Thenmozhi, S. (IJRDO -Journal of Computer Science Engineering, 2017)
The healthcare industry today generates large amounts of complex data data about patients, hospital resources, disease diagnosis, electronic patient records and medical devise. Data mining is a collection of algorithmetic ways to extract informative patterns from raw data. In this paper, the researcher describe a novel methodology which employs machine learning as an alternative means to explore hospital characteristics and client satisfaction, for decision making and improved quality of care. Applied well known feature selection and data mining algorithms such as forward selection and Naïve Bayes respectively, to determine patient satisfaction. The dataset comprised of three types of data which are patient perception about received care, Nurse perception about the working environment and Organizational attributes of the hospital. The experimental results exhibited high classification accuracy 91.3%, allowing valid conclusions to be reached about the organizational and workforce factors which attribute to patient satisfaction. The findings were validated using traditional statistical methods such as binomial correlation and linear regression.. The result of the study will be beneficial to current and future SMEs in India.