Disease can book a doctor’s appointment using this

Disease Predicting System Using Data Mining Techniques Age, sex, cholesterol level, chest pain, angina, maximum heart rate, fasting blood sugar, exercise. MAFIA (Maximal Frequent Item set Algorithm); C4.5 Algorithm; K-means clustering The datasets have a small sample space. 94% accurate for a particular data of a region.

 I.            PROPOSED WORKThis applicationwill consist of a questionnaire whichthe user will have to answer.This will be related to symptoms ofthe patient and his habits.

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This will be takenas input and by using decision treesalgorithm the possibility of diabetes canbe predicted. The user can enterhis/her diet. This app will monitor it and give the required analysis.

The medication consumed by the user will alsobe monitored. The user can book a doctor’s appointment using this app. The details of the appointment willbe sent to the doctor.The following are the modules of the system:1.  Prediction2.

  Diagnosis3.  Diet plan4.  Reminders andalarm and5.  Orderingmedicines.This system will accurately predict whether the user is prone to diabetes ornot, by use of the prediction algorithm computations. The users can registerthemselves to createtheir respective accounts in the system.

User can access to thesystem through a login interface. The user can login using username andpassword on which the user will be directed to the home page of the systemThe user can then enter data forprediction of diabetes. We are mainly focusing on Mellitus type ofdiabetes. The user can also use the additional features like maintaining a diet,reminding of dosage, call for an appointment of a doctor, etc. The prediction will be done using C5 Classifier Decision Tree. The  following are the data sets which are goingto be considered as parameters in the prediction algorithm: 1.  Number of times pregnant2.   Plasma glucose concentration a 2 hours in an oral glucose tolerance test3.

  Diastolic bloodpressure (mm Hg)4.  Triceps skin fold thickness (mm)5.  2-Hour seruminsulin (mu U/ml)6.

  Body mass index (weight in kg/(height in m)^2)7.  Diabetes pedigree function8.  Age (years)9.  Class variable (0 or 1)


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