DocumentCode :
2026952
Title :
Effect of feature selection on machine learning algorithms for more accurate predictor of surgical outcomes in Benign Pro Static Hyperplasia cases (BPH)
Author :
Megherbi, D.B. ; Soper, B.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Massachusetts, Lowell, MA, USA
fYear :
2011
fDate :
19-21 Sept. 2011
Firstpage :
1
Lastpage :
7
Abstract :
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, Artificial Intelligence (AI) algorithms were used to train computers to predict the surgical outcome in BPH patients treated by TURP or VLAP. Our aim was to investigate whether, based on eleven clinical biomarker features, AI can reproduce the clinical outcome of known cases and assist the urologist in predicting surgical outcomes. In this paper, the objective is to perform data analysis to investigate if specific features have a greater impact on predicting whether the patients had the desired outcome after a surgical procedure is done. Finally, how the number of significant features ought to be weighted to predict the outcome after surgery, is determined to create the most accurate prediction method. Here both the Decision Tree and Naïve Bayse machine learning methods are used and compared.
Keywords :
Bayes methods; data analysis; decision trees; feature extraction; learning (artificial intelligence); medical computing; surgery; AI algorithm; BPH case; BPH patient treatment; Benign Pro Static Hyperplasia case; Naive Bayes machine learning method; TURP treatment; VLAP treatment; artificial intelligence algorithm; clinical biomarker features; clinical outcome prediction; data analysis; decision tree; feature selection; machine learning algorithm; minimally invasive treatment; multiple assessment parameters; surgical outcome predictor; urologist; Accuracy; Artificial intelligence; Bladder; Decision trees; Prediction algorithms; Surgery; Training data; Artificial Intelligence; Benign Pro Static Hyperplasia; Distributed Systems; Machine learning; Mobile Robotics; Multi-agent systems; Reinforcement Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications (CIMSA), 2011 IEEE International Conference on
Conference_Location :
Ottawa, ON, Canada
ISSN :
2159-1547
Print_ISBN :
978-1-61284-924-9
Type :
conf
DOI :
10.1109/CIMSA.2011.6059938
Filename :
6059938
Link To Document :
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