Fuzzy logic applied to a Patient Classification System

Rosati, Samanta; Montanaro, Aldo; Tralli, Augusta; Balestra, Gabriella

doi:10.1109/embc.2013.6609749

Cited by 2 publications

(1 citation statement)

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“…An important characteristic of FL is the possibility of obtaining an explanation of results. During the most recent decade, FL was often used in the medical field for the implementation of decision-support systems, with promising results [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Checcucci

Rosati

Cillis

et al. 2023

JCM

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The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases.

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Section: Introductionmentioning

confidence: 99%