A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach
Display Omitted This paper uses Principle Component Analysis (PCA) for the dimensionality reduction of attributes in the Pima Indian Diabetes Data set (PID).This method uses false split point calculation to eliminate false split points in FSDT approach.The best attribute is identified using gini index measure.New fuzzy membership function is used to convert crisp set into fuzzy values.This approach given 76.8% average test accuracy with PID data set which is available in the UCI machine learning repository. Knowledge inference systems are built to identify hidden and logical patterns in huge data. Decision trees play a vital role in knowledge discovery but crisp decision tree algorithms have a problem with sharp decision boundaries which may not be implicated to all knowledge inference systems. A fuzzy decision tree algorithm overcomes this drawback. Fuzzy decision trees are implemented through fuzzification of the decision boundaries without disturbing the attribute values. Data reduction also plays a crucial role in many classification problems. In this research article, it presents an approach using principal component analysis and modified Gini index based fuzzy SLIQ decision tree algorithm. The PCA is used for dimensionality reduction, and modified Gini index fuzzy SLIQ decision tree algorithm to construct decision rules. Finally, through PID data set, the method is validated in the simulation experiment in MATLAB.