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NumPy is a Python library for performing numerical data structures and specialized computing. It improves the components of N-dimensional arrays and provides operations and tools to interface with these arrays. NumPy implements the N-dimensional array, or ndarray, and provides Python-specific scientific methods for performing realistic array and matrix operations. When compared to array programming in other languages, it will allow us to do a wide range of mathematical operations and data manipulations. NumPy can be used with other Python packages and programming languages such as C and C++. NumPy now supports object-oriented programming as well. For example, a class called ndarray may be an N-dimensional array with multiple ways of performing various data structure operations and characteristics.

NumPy is a Python library for performing numerical data structures and specialized computing. It improves the components of N-dimensional arrays and provides operations and tools to interface with these arrays. NumPy implements the N-dimensional array, or ndarray, and provides Python-specific scientific methods for performing realistic array and matrix operations. When compared to array programming in other languages, it will allow us to do a wide range of mathematical operations and data manipulations. NumPy can be used with other Python packages and programming languages such as C and C++. NumPy now supports object-oriented programming as well. For example, a class called ndarray may be an N-dimensional array with multiple ways of performing various data structure operations and characteristics.

Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. It is divided into two major type's type1 and type2 diabetes. During the past decade, diabetes mellitus has emerged as an important clinical and public health problem throughout the world, especially in developing country like Ethiopia (800,000 patients in 2000 E.C). Extracting implicit knowledge from domain expert about diabetes was so difficult in order to get strong rules about diabetes. Data Mining is a machine learning algorithm which predicts or extracts unseen knowledge contained in large databases; data mining classifiers are the algorithms which classify the inputted data to the correct class it belongs. In this study j48 tree was used in order to generate rules from the input diabetes data set. A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The integrated system used javaNetbean to integrate generated rules from j48 classifier by calling predicted results from weka to swi-prolog through jpl_call library. Finally the system performance and user acceptance was tested on the developed system by domain experts based on the prepared test cases. The developed prototype scored 91.16% overall average from the user .Hence the integrated prototype is good for predicting diabetes types and treatments in the domain of health.