Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques  

作　　者：Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires 

机构地区：[1]Department Information System Study Program,Faculty of Science and Technology,Universitas Airlangga,Surabaya,60286,Indonesia [2]School of Software,Northwestern Polytechnical University,Xi’an,710072,China [3]Research Center for Quantum Engineering Design,Faculty of Science and Technology,Universitas Airlangga,Surabaya,60286,Indonesia [4]School of Computer Science and Software Engineering,Shenzhen University,Shenzhen,518061,China [5]Cybersecurity Center,Prince Mohammad Bin Fahd University,617,Al Jawharah,Khobar,Dhahran,34754,Saudi Arabia [6]Cardiff School of Technologies,Cardiff Metropolitan University,Western Avenue,Cardiff,CF52YB,UK [7]Instituto de Telecomunicacoes,Escola Superior de Tecnologia e Gestao de Agueda,Universidade de Aveiro,águeda,3750-127,Portugal

出　　处：《Computers, Materials & Continua》2025年第3期5115-5134,共20页计算机、材料和连续体(英文)

基　　金：funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.

摘　　要：This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关 键 词：Fuzzy K-nearest neighbor artificial neural network accuracy precision RECALL F-MEASURE CHI-SQUARE best search first heart stroke 

分 类 号：R74[医药卫生—神经病学与精神病学]