Using Efficient SUPANOVA Kernel for Heart Disease Diagnosis


Many machine learning methods focus on the quality of prediction results as their final purpose. Spline kernel based methods attempt to provide also transparency to the prediction identifying features that are important in the decision process. In this paper, we present a new heuristic for computing efficiently sparse kernel in SUPANOVA. We applied it to a benchmark Boston housing market dataset and to socially important problem of improving the detection of heart diseases in the population using a novel, non-invasive measurement of the heart activities based on magnetic field produced by the human heart. On this data, 83.7% predictions were correct, exceeding the results obtained using the standard Support Vector Machine and equivalent kernels. Equally good results were achieved by the spline kernel on a benchmark Boston housing market dataset.

  • Abstract
  • Introduction
  • Supanova and Its Implementation
  • Performance Measurements and Data Benchmarks
  • Conclusions
  • Acknowledgement
  • References

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