GDD: Geometrical driven diagnosis based on biomedical data

Abstract

Modern medical diagnosis heavily rely on bio-medical and clinical data. Machine learning algorithms have proven effectiveness in mining this data to provide an aid to the physicians in supporting their decisions. In response, machine learning based approaches were developed to address this problem. These approaches vary in terms of effectiveness and computational cost. Attention has been paid towards non-communicable diseases as they are very common and have life threatening risk factors. The diagnosis of diabetes or breast cancer can be considered a binary classification problem. This paper proposes a new machine learning based algorithm, Geometrical Driven Diagnosis (GDD), to diagnose diabetes and breast cancer with accuracy up to 99.96% and 95.8% respectively.

Keywords

• GDD
• Medical diagnosis
• Classification
• Machine learning
• Diabetes
• Big data