Data Mining Inventory System For Healthcare Management

Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision-making.The methodology includes the consultation of different individuals ranging from staff, patients and some knowledgeable people in the field. Other methods of information generation adopted includes relevant literature(such as textbooks, journals etc.) and surfing the internet.The method of implementation was the use of ASP.NET MVC4, Microsoft SQL server 2008, Microsoft Visio Studio 2012 and C# programming language. The basic user consideration was given preference when designing the system. Therefore, this project if embraced would address the problems of the existing system. This application stores the details records about the patient and also allows the clinic to view various details information. The different modules like information center and inquiry center are developed in the front-end C Sharp(C#). Corresponding tables are developed in the back-end and the connectivity is established. The Analysis and Feasibility study give the entire information about the project.This research work has also developed an appropriate computer-based information and/or decision support systems in Heart Disease Prediction System using data mining modeling technique namely, Naive Bayes or Bayes’ Rule which is the basis for many machine learning and data mining methods. The rule(algorithm) is used to create models with predictive capabilities. It provides new ways of exploring and understanding data. It learns from the “evidence” by calculating the correlation between the target(i.e. dependent) and other(i.e. independent) variables. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. Finally, the thesis highlights the limitations of data mining and discusses some future directions.