A Comparison of Decision Tree Algorithms on Healthy Eating System

Kamble Rita R.

Now daysmany people suffers from various diseases due to poor eating habits. In the current scenario fast food become important food in daily routine because it is effortlessly available but taking fast food in routine may cause for disease like heart attack, diabetics etc. Healthier diets help us to maintain our health and keep us away from many diseases.A healthy diet may improve or maintain optimal health. In developed countries, affluence enables unconstrained caloric intake and possibly inappropriate food choices. Medical study has revealed that people set a bigger possibility of warding off illness by consumption of healthy foods and by increasing their resistant system. For better recovery from diseases or surgery etc individual have special needs according to their medical profile, cultural backgrounds and nutrient requirements. Design and implementation of healthy diet recommendation system is based on web data mining which is the application of data mining technique help us to determine pattern from web. In terms of accuracy and time performance analysis we are using two decision tree learning algorithm ID3 and C4.5 and apply it on healthy diet application.In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains.