MOCC medical diagnosis support system for life styled diseases.

Abstract

The main reason that brought the scope of developing medical diagnosis support system for life style diseases is the high death rate and huge amount of people suffering from such diseases. The system is a web based application developed using PHP, JavaScript and MySQL database using Adobe Dreamweaver CC 2018 software and XAMPP. The study was conducted weather it was feasible to come up with a system in terms of economic, technological, operational and social standings and it was determined that it was viable in these aspects. Data was collected through the use of interviews and questioners the researcher targeted the patients and the doctors. Data gathering was done to identify problems faced by the Ministry of health and child care and patients. From the data gathered the researcher determined requirements and how the new system was going function. The system analysis process was done successfully and all the necessary data and process flow were modelled using various techniques including data flow diagrams and class diagrams to ensure that nothing was left out for the design and development of the system. Evaluations was made on how to deliver the new system and amongst all alternatives in-house development was favoured due to availability of developers within the organisation. The design of the system was done focusing on all system aspects that is architectural design, physical design, database design and logical design. The system was tested using Blackbox and White box testing and all the necessary verifications of data and system validation was done to ensure the system is working as intended. Parallel changeover was used to install the system this is because visiting to the hospital direct is not going to be faced out its either use the system or go direct to the hospital. Maintenance of the system was decided that all the methods of maintenance is going to be used since they save different purposes. The system will allow diagnosis by matching symptoms with appropriate diseases and suggest specialist according to diseases diagnosed in area of speciality.