Abstract: Methods and system for detection and diagnosis of diseases including cancer. A patient sample is analyzed by absorbance spectroscopy in the near- and mid-infrared range to produce a spectrometric signature, the sample obtained from a patient. The signature is processed by a computational engine using one or more machine learning techniques to determine whether the spectrometric signature indicates the presence of a disease, including one or more cancers. Embodiments operate outside of conventionally accepted wavelength ranges, facilitating more rapid, simple, and reliable testing.