Human diseases diagnosis system (HDDS) / Aida Raihana Abd Wahab
Material type:
TextPublication details: Kuantan, Pahang : UMP, 2012Description: xiv, 49 p. : ill. (some col.) ; 30 cm. + 1 CD- ROMISBN: - THE0001738(Local)
| Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
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Final Year Report
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UMPLIB GAMBANG | CD 6514 | QA76.66 .A33 2012 rs Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000068605 | ||
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Final Year Report
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UMPLIB PEKAN | QA76.66 .A33 2012 rs Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000068604 |
Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2012
Bibliography : p. 49
As we know that human disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based expert system dealing with human disease diagnosis has to overcome various difficulties. Detecting diseases at early stage can enable to overcome and treat them appropriately .Identifying the treatment accurately depends on the method that is used in diagnosing the diseases. Human Diseases Diagnosis Systems (HDDS) is an expert system that used to give earlier diagnosis of four major diseases, Chikungunya, Avian Influenza, H1N1 and Dengue. The idea to build the system by using expert systems is because expertise is not always available so that users can do their check up on the symptoms. So, by using this system can help them to give earlier diagnosis based on the questionnaires provided and lastly will generate the result of the disease. Besides that, user can get information of the diseases and be aware of the symptoms. This system is build by using PHP and MySQL as the database. Basic structure of rule based expert system are knowledge base, the database and the inference engine, explanation facilities and lastly is the user interface. The knowledge base for this system contains the knowledge useful for problem solving which is represented as a set of rules. The database includes a set of facts that used to match against the IF condition parts of rules that stored in the knowledge base. There are two types of inference which are forward and backward chaining. As for this system, it used forward chaining as its inference engine. This is because the reasoning is from facts to conclusion. Finally, it is hoped that this system can provide benefits to the users.