Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

Abstract

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

Reducing the time needed to solve a traveling salesman problem by clustering with a Hierarchy-based algorithm

“…The issues with clustering algorithms can be summarized as scalability and the execution time for big data. Here, we want to explain the BIRCH algorithm [8] which is an integrated hierarchical clustering algorithm. Birch is a multi-stage method in the field of hierarchical clustering techniques that solves two hierarchical clustering difficulties, which are the scalability and rollback problems.…”