Abstract:

When designing medical diagnostic programs, disease prediction is considered as a captive task. Machine learning (ML) approaches were successfully used in a variety of applications, which includes the medical diagnoses. Through the development of a classification system, a ML algorithm could greatly aid in solving health-related problems that can help clinicians predict and diagnose diseases and provide patients with treatment at an early stage. This work aims to detect diabetes using ML techniques as decision tree and k-nearest neighbor (KNN) and on the basis of data that is manually collected from Iraqi population society. The work discuss the comparison of such algorithms in terms of accuracy of results..

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