Effect of feature selection on machine learning algorithms for more accurate predictor of surgical outcomes in Benign Pro Static Hyperplasia cases (BPH)

Predicting the clinical outcome prior to minimally invasive treatments for Benign Prostatic Hperlasia (BPH) cases would be very useful. However, clinical prediction has not been reliable in spite of multiple assessment parameters, such as symptom indices and flow rates. In our prior study, Artificia...

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Bibliographische Detailangaben

Hauptverfasser:	Megherbi, D. B., Soper, B.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Accuracy Artificial intelligence Benign Pro Static Hyperplasia Bladder Decision trees Distributed Systems Machine learning Mobile Robotics Multi-agent systems Prediction algorithms Reinforcement Learning Surgery Training data
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