Ruslan Sorano

• Sorano, Ruslan; Ripon, Kazi Shah Nawaz & Magnusson, Lars Vidar (2024). Evolutionary Optimization of Artificial Neural Networks and Tree-Based Ensemble Models for Diagnosing Deep Vein Thrombosis. I Westphal, Florian; Peretz-Andersson, Einav; Riveiro, Maria; Bach, Kerstin & Heintz, Fredrik (Red.), 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden. Swedish Artificial Intelligence Society. ISSN 9789180757096. s. 178–187. doi: 10.3384/ecp208020. Fulltekst i vitenarkiv Vis sammendrag

  Machine learning algorithms, particularly artificial neural networks, have shown promise in healthcare for disease classification, including diagnosing conditions like deep vein thrombosis. However, the performance of artificial neural networks in medical diagnosis heavily depends on their architecture and hyperparameter configuration, which presents virtually unlimited variations. This work employs evolutionary algorithms to optimize hyperparameters for three classic feed-forward artificial neural networks of pre-determined depths. The objective is to enhance the diagnostic accuracy of the classic neural networks in classifying deep vein thrombosis using electronic health records sourced from a Norwegian hospital. The work compares the predictive performance of conventional feed-forward artificial neural networks with standard tree-based ensemble methods previously successful in disease prediction on the same dataset. Results indicate that while classic neural networks perform comparably to tree-based methods, they do not surpass them in diagnosing thrombosis on this specific dataset. The efficacy of evolutionary algorithms in tuning hyperparameters is highlighted, emphasizing the importance of choosing the optimization technique to maximize machine learning models' diagnostic accuracy.

• Sorano, Ruslan; Ripon, Kazi Shah Nawaz; Magnusson, Lars Vidar; Halstensen, Thor-David & Ghanima, Waleed (2024). Enhancing Deep Vein Thrombosis Diagnosis with Multi-Objective Evolutionary Algorithm and Machine Learning. I Ogul, Hasan; Aamodt, Jonas Moræus; Misra, Sanjay & Kjølerbakken, Kai Morgan (Red.), 2024 4th International Conference on Applied Artificial Intelligence (ICAPAI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350349764. s. 1–1. doi: 10.1109/ICAPAI61893.2024.10541216. Fulltekst i vitenarkiv
• Sorano, Ruslan; Ripon, Kazi Shah Nawaz & Magnusson, Lars Vidar (2024). Multi-Objective Evolutionary Optimization for Tuning Hyperparameters and Reducing Features in Prediction of Deep Vein Thrombosis. I Xue, Bing (Red.), 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350308365. doi: 10.1109/CEC60901.2024.10612191. Fulltekst i vitenarkiv
• Sorano, Ruslan; Ripon, Kazi Shah Nawaz & Magnusson, Lars Vidar (2023). Evolutionary Multi-objective Optimization of Hyperparameters for Decision Support in Healthcare. I Kulkarni, Anand J & Gandomi, Amir H. (Red.), Handbook of Formal Optimization. Springer Nature. ISSN 9789811988516. doi: 10.1007/978-981-19-8851-6_28-2. Fulltekst i vitenarkiv
• Sorano, Ruslan; Magnusson, Lars Vidar & Abbas, Khurshid (2022). Comparing Effectiveness of Machine Learning Methods for Diagnosis of Deep Vein Thrombosis. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. s. 279–293. doi: 10.1007/978-3-031-10548-7_21. Fulltekst i vitenarkiv

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