Low-Power Analog Integrated Architecture of the Voting Classification Algorithm for Diabetes Disease Prediction.

Abstract

A low-power (∼ 600nW), fully analog integrated architecture for a voting classification algorithm is introduced. It can effectively handle multiple-input features, maintaining exceptional levels of accuracy and with very low power consumption. The proposed architecture is based on a versatile Voting algorithm that selectively incorporates one of three key classification models: Bayes or Centroid, or, the Learning Vector Quantization model; all of which are implemented using Gaussian-likelihood and Euclidean distance function circuits, as well as a current comparison circuit. To evaluate the proposed architecture, a comprehensive comparison with popular analog classifiers is performed, using real-life diabetes dataset. All model architectures were trained using Python and compared with the software-based classifiers. The circuit implementations were performed using the TSMC 90 nm CMOS process technology and the Cadence IC Suite was utilized for the design, schematic and post-layout simulations. The proposed classifiers achieved sensitivity of ≥ 96.7% and specificity of ≥ 89.7%.