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Authors: Luigi Lella 1 ; Ignazio Licata 1 and Christian Pristipino 2

Affiliations: 1 ISEM, Ins. For Scientific Methodology, PA, Italy ; 2 Interventional and Emergency Cardiology Unit, San Filippo Neri – ASL Roma 1, Rome, Italy

Keyword(s): Artificial Intelligence, Data Mining, Computational Optimization.

Abstract: A new predictive model tested on the Pima Indians Diabetes Database is presented. This model represents a particular subclass of A-Type Unorganized Turing machine (UTM), where the state is unique. It appears as a simple combinational network of NAND gates (it is not the more generic sequential type described by Turing, but it is enough to solve the examined predictive task). The optimal architecture of this network is identified by the use of evolutionary algorithms, which are therefore used as computational optimization algorithms. In particular, a classic genetic algorithm and an hybrid evolutionary-swarm algorithm that we have called Evolutionary Bait Balls Model (EBBM) were tested for this purpose. The predictive model thus defined, made it possible to achieve higher performances than those obtained with other classic predictive models. The final combinational network of NAND gates obtained through our model has allowed us to identify a simple Boolean rule to determine the existe nce of the risk of incurring diabetes mellitus. (More)

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Paper citation in several formats:
Lella, L., Licata, I. and Pristipino, C. (2022). Pima Indians Diabetes Database Processing through EBBM-Optimized UTM Model. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 384-389. DOI: 10.5220/0010806500003123

@conference{healthinf22,
author={Luigi Lella and Ignazio Licata and Christian Pristipino},
title={Pima Indians Diabetes Database Processing through EBBM-Optimized UTM Model},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF},
year={2022},
pages={384-389},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010806500003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF
TI - Pima Indians Diabetes Database Processing through EBBM-Optimized UTM Model
SN - 978-989-758-552-4
IS - 2184-4305
AU - Lella, L.
AU - Licata, I.
AU - Pristipino, C.
PY - 2022
SP - 384
EP - 389
DO - 10.5220/0010806500003123
PB - SciTePress

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