Use of Machine Learning Techniques for Predicting Heart Disease Risk from Phone Enquiries Data
Martin-Rodriguez, F.; Pajaro-Lorenzo, J.; Isasi-de-Vicente, F.; Fernandez Barciela, M.
Show abstract
This paper is about the application of known machine learning (ML) techniques for the prediction of heart disease risk. A public database is used to train and test the ML models. Results are evaluated using standard measures like precision, recall and F-score. ML models selected are well known techniques and they are based on different approaches. Chosen methods are: MLP (Multi-Layer Perceptron), SVM (Support Vector Machine) and Bagged Tree (Bootstrap Aggregated Trees). After evaluating techniques alone on their own, a new "triple voting method" (TVM) is tested applying the three individual methods and "adding" their results to improve accuracy.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Navigating the Multiverse: A Hitchhiker’s Guide to Selecting Harmonisation Methods for Multimodal Biomedical Data 93%
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies 92%
- Linking Patient Records at Scale with a Hybrid Approach Combining Contrastive Learning and Deterministic Rules 92%
Similar papers in this journal
- Hilbert-Envelope Features for Cardiac Disease Classification from Noisy Phonocardiograms 93%
- Improved online event detection and differentiation by a simple gradient-based nonlinear transformation: Implications for the biomedical signal and image analysis 93%
- Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.