The min-norm (MN) method is shown to improve estimates of delay profile and Doppler spectrum from frequency modulated continuouswave (FMCW) channel data corrupted with in-band interference. Results of MN estimates are examined by comparing with those obtained from FFT processing.Introduction: Conventional techniques for estimating the power delay profile (PDP) and Doppler spectrum from a frequency modulated continuous-wave (FMCW) sounder are based on FFT processing. However, these techniques fail to perform well in the presence of in-band interference. The interference increases the noise floor in the average PDP (APDP) and Doppler spectra, and this undermines the ability to detect weak multipath components. Previous attempts to reduce the effect of in-band interference have included clipping the level of interference [1] (but the retained data will distort the signal) or Prony modelling of data [2] (which requires some interference-free samples at the beginning of the data sequence). In this Letter, the min-norm (MN) method, a sub-space spectral estimation method, is used to estimate the APDP and Doppler spectrum from interference corrupted data.
Purpose:
This paper compares five supervised learning algorithms (support vector machines, k-nearest neighbor, decision tree, random forest, and AdaBoost) for predicting heart disease and examines the impact of normalization and GridSearch hyper-parameter tuning on model performance.
Methods:
The study utilizes the Cleveland database from the University of California-Irvine (UCI) repository, comprising data on 918 instances of heart disease patients with 12 attributes. Eleven attributes serve as predictors, while one attribute represents the target class. Models are built and tested using this dataset.
Results:
Comparing the algorithm performances with existing literature, accuracies range from 89.13–91.85%. AdaBoost exhibits the highest performance, whereas the decision tree performs the least effectively. Results surpass those reported in the literature. Normalization improves prediction performance by 17% for Support Vector Machines (SVM) and 14% for k-nearest neighbor (kNN). SVM does not benefit from GridSearch, while GridSearch enhances the decision tree and AdaBoost by 7% and 4% respectively. Normalization combined with GridSearch improves kNN and random forest by 2–3%.
Conclusion:
This study compares supervised learning algorithms for heart disease prediction. AdaBoost emerges as the top-performing algorithm, while the decision tree performs relatively poorly. The findings surpass those in the literature. Normalization significantly improves performance for SVM and kNN, while GridSearch enhances the decision tree and AdaBoost. Combined, normalization and GridSearch yield performance improvements for kNN and random forest. These results contribute to the field of heart disease prediction, offering valuable insights for algorithm selection and guiding future research.
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