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.

现代医学诊断在很大程度上依赖于生物医学和临床数据。事实证明，机器学习算法可有效地挖掘此数据，以帮助医师支持他们的决策。作为响应，开发了基于机器学习的方法来解决此问题。这些方法在有效性和计算成本方面有所不同。由于非传染性疾病非常普遍并具有威胁生命的危险因素，因此已引起关注。糖尿病或乳腺癌的诊断可以视为二元分类问题。本文提出了一种新的基于机器学习的算法，即几何驱动诊断（GDD），可准确诊断糖尿病和乳腺癌，准确率分别达到99.96％和95.8％。