Genetic Programming-based induction of a glucose-dynamics model for telemedicine

Falco, Ivanoe De; Cioppa, Antonio Della; Koutný, Tomáš; Krčma, Michal; Scafuri, Umberto; Tarantino, Ernesto

doi:10.1016/j.jnca.2018.06.007

Cited by 26 publications

(7 citation statements)

References 21 publications

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“…De Falco et al [15] presented a work on GP-based induction of a glucose-dynamics model for telemedicine. The work aims to create a regression model that allows the determination of the BG value from interstitial glucose in patients with T1DM with the idea of using it in a telemedicine portal.…”

Section: Related Workmentioning

confidence: 99%

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

Contador

Colmenar

Garnica

et al. 2021

Genet Program Evolvable Mach

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In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the root-mean-square error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions in diabetic patients. In this work, we use two datasets to analyse two different scenarios: What-if and Agnostic, the most common in daily clinical practice. In the What-if scenario, where future events are evaluated, results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis by reducing the number of dangerous mispredictions. In the Agnostic situation, with no available information about future events, results suggest that we can obtain good predictions with only information from the previous hour for both Grammatical Evolution and Multi-Objective Grammatical Evolution.

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Section: Related Workmentioning

confidence: 99%

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

Contador

Colmenar

Garnica

et al. 2021

Genet Program Evolvable Mach

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“…In GP literature, the automatic problem solving ability of GP techniques is widely utilized in symbolic regression problems for datadriven modeling works (Dal Piccol Sotto and De Melo 2014). The GP method has been used extensively in many application areas, for example production scheduling (Nguyen et al 2017), optimal water reservoir-operating (Ashofteh et al 2015), energy of residential buildings (Castelli et al 2015;Kaboli et al 2017;Tahmassebi and Gandomi 2018), educational technologies (Zafra and Ventura 2012), urban planning (Patnaik and Bhuyan 2016), geotechnical design (Keshavarz and Mehramiri 2015), hydrology (Shoaib et al 2015), medicine (De Falco et al 2018). Also, GP has been widely utilized in many computer science problems such as classification problems (Tran et al 2016) (Kuo et al 2007), computer vision (Liu et al 2016), image processing (Shao et al 2014) (Liang et al 2020), signal processing (Feli and Abdali-Mohammadi 2019), artificial neural network design (Suganuma et al 2017).…”

Section: Genetic Programming Preliminaries and Gpols Algorithmmentioning

confidence: 99%

DEHypGpOls: A Genetic Programming with Evolutionary Hyper-Parameter Optimization and its Application for Stock Market Trend Prediction

Ari

Alagöz

2022

Preprint

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Stock markets are one of the most popular financial markets since they can bring high revenues to their investors. In order to reduce the risk factor for investors, intelligent and automated stock market trend prediction tools are developed by using computational intelligence methods. This study presents a hyper-parameter optimal Genetic Programming (GP) model generation framework for a day-ahead prediction of stock market index trends. In order to obtain the best trend prediction model from the stock market data, a Differential Evolution (DE) algorithm is implemented to optimize hyper-parameters of a GP algorithm with Orthogonal Least Square (GpOls). Evolutions of GpOls agents within the hyper-parameter search space allow adaptation of GpOls algorithm for a given training dataset by enabling optimal autotuning of user-defined parameters. Authors demonstrated that this structure can significantly improve modeling performances of GpOls algorithms for data-driven modeling applications. In the study, the proposed DE based hyper GpOls (DEHypGpOls) algorithm is implemented for a day-ahead forecast problem of the Istanbul Stock Exchange 100 Index (ISE100) trends. In the experimental study, real trend data from ISE100 and seven other international stock markets are used to extract a day-ahead forecast model of daily ISE100 trends. The forecast model of the DEHypGpOls algorithm can provide 68% prediction accuracy for the test dataset. In ISE100 index market simulations, daily exchanges of ISE100 index according to buy or sell signal of the forecast model of DEHypGpOls provided about 273% more income compared to the income of a long-term investment strategy.

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Section: Genetic Programming Preliminaries and Gpols Algorithmmentioning

confidence: 99%

DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

Ari

Alagöz

2022

Soft Comput

View full text Add to dashboard Cite

Stock markets are one of the most popular financial markets since they can bring high revenues to their investors. In order to reduce the risk factor for investors, intelligent and automated stock market trend prediction tools are developed by using computational intelligence methods. This study presents a hyper-parameter optimal Genetic Programming (GP) model generation framework for a day-ahead prediction of stock market index trends. In order to obtain the best trend prediction model from the stock market data, a Differential Evolution (DE) algorithm is implemented to optimize hyperparameters of a GP algorithm with Orthogonal Least Square (GpOls). Evolutions of GpOls agents within the hyper-parameter search space allow adaptation of GpOls algorithm for a given training dataset by enabling optimal autotuning of user-defined parameters. Authors demonstrated that this structure can significantly improve modeling performances of GpOls algorithms for data-driven modeling applications. In the study, the proposed DE based hyper GpOls (DEHypGpOls) algorithm is implemented for a day-ahead forecast problem of the Istanbul Stock Exchange 100 Index (ISE100) trends. In the experimental study, real trend data from ISE100 and seven other international stock markets are used to extract a day-ahead forecast model of daily ISE100 trends. The forecast model of the DEHypGpOls algorithm can provide 68% prediction accuracy for the test dataset. In ISE100 index market simulations, daily exchanges of ISE100 index according to buy or sell signal of the forecast model of DEHypGpOls provided about 273% more income compared to the income of a long-term investment strategy.

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Genetic Programming-based induction of a glucose-dynamics model for telemedicine

Cited by 26 publications

References 21 publications

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

DEHypGpOls: A Genetic Programming with Evolutionary Hyper-Parameter Optimization and its Application for Stock Market Trend Prediction

DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

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