Title :
Predictive Data Mining and pattern recognition in the medical sector: Implementation and experience
Author :
Shabbir, Aqsa ; Ansari, Yasmin Tauqeer ; Kazim, Ali Hussain ; El-Hassan, Ammar
Author_Institution :
Dept. of Electr. & Electron. Eng., Lahore Coll. of Women Univ., Lahore, Pakistan
Abstract :
Large enterprises essentially require a reliable infrastructure that can store, retrieve and analyze the massive volumes of high dimensional data. Knowledge Discovery in Databases (KDD) through Data Mining (DM) presents a powerful tool for storing and retrieving data in a manner that optimizes performance as well as resources. This work presents the application of Time Series Data Mining Algorithm to Hospital Management Information System (HMIS) in a public sector hospital in Pakistan. Public sector hospitals in Pakistan, apart from being typically overcrowded are severely limited by lack of resources. This research focuses on the cost effective application of KDD alongside correctly predicting disease patterns, hospital admittance rate and patient turn out. The results of this work not only bring about an improvement in management for this hospital but also provide a model for other health care facilities in the developing world.
Keywords :
data mining; diseases; health care; hospitals; information retrieval; medical information systems; optimisation; time series; HMIS; KDD; Pakistan; data retrieval; data storage; disease pattern prediction; health care; hospital admittance rate; hospital management information system; knowledge discovery in database; medical sector; pattern recognition; performance optimization; predictive data mining; public sector hospital; time series data mining algorithm; Cancer; Data mining; Databases; Reliability; Surgery; Data Mining; Hospital Management Information System; Knowledge Discovery in Databases; Time Series algorithm; decision making;
Conference_Titel :
Computer Applications and Information Systems (WCCAIS), 2014 World Congress on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4799-3350-1
DOI :
10.1109/WCCAIS.2014.6916645