Ahmad Nasharathul Na'aim B. Baharuddin by Nash Na'aim https://padlet.com/nash90imtihan/nashnaim M061410013 en-us 2015-04-09 07:31:10 UTC 2015-04-09 08:15:11 UTC hello@padlet.com https://padlet-assets.storage.googleapis.com/portrait/smiley.jpg Decision Support System for the Intelligent Identification of Alzheimer using Neuro Fuzzy logic nash90imtihan https://padlet.com/nash90imtihan/nashnaim/wish/56260084 Intoduction

Neuro-fuzzy system combines the advantages of both whilst eliminating their disadvantages. Using neuro-fuzzy logic the diagnosis of Alzheimer will provide a self-learning intelligent system that is capable of handling uncertainties in the diagnosis process.

Discussion

A system was designed which consists

of asset of symptoms needed for the diagnosis:-

(1) Memory loss that affects day-to-day function, (2) Difficulty performing familiar tasks, (3) Problems with language, (4) Disorientation of time and place, (5) Poor or decreased judgment, (6) Problems with abstract thinking, (7) Misplacing things, (8) Changes in mood and behavior and (9) Changes in personality and Loss of initiative.

The set of symptoms are fed into network. The patient is expected to choose from the list of symptoms the one corresponding to what he/she is having, if:-

  - Having 5 and above of the symptoms is having severe Alzheimer

- Having approximately 4 of the symptoms is might be suffering from Alzheimer

- Having 3 or less of the symptoms is may not be having Alzheimer

Then, they created fuzzy logic membership functions. These are graphical function representation of the magnitude of the preparation of each input that is processed. The fuzzy set however is zero, X/4 or one. Hence the corresponding of the fuzzy as per below:-

- Between zero and X/4, the person’s condition is Low (“not suffering from Alzheimer”)

- In-between zero and one, the condition is moderate (“might be suffering from Alzheimer”)

- Between X/4 and one, the person’s condition is high (“suffering from severe Alzheimer”)

Defuzzification coverts the rules base fuzzy output into non-fuzzy (numerical values). It reflects the 

interpretation of the logic of the different linguistic variable. The system can also be configured to 

handle not only Alzheimer but, other kind of illness and diseases.


Conclusion

A hybrid neuro–fuzzy system is able to help in diagnosis of Alzheimer using a set of symptoms. This system which uses a set of fuzzified data set incorporated into neural network system is more precise than the traditional system. The system designed is an interactive system that tells the patient his current condition as regards Alzheimer.

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