Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Zou, Shujie; Chu, Chiawei; Shen, Ning; Ren, Jia

doi:10.3390/math11234778

Cited by 3 publications

(4 citation statements)

References 20 publications

(20 reference statements)

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“…So far, many control techniques have been successfully implemented in real time, from numerical integration methods to RHC. As in many other chemical or biochemical processes, every batch must be optimally controlled (with the same parameters [27,28]). The authors have been involved in a real-time control application concerning microalgae growth [8] that could be addressed in a future project using the ML controller.…”

Section: Discussionmentioning

confidence: 99%

“…Extending the applicability of the EA predictions is a challenge, involving techniques and control structures diminishing the execution time [26]. One can consider that our work addresses the execution time's decreasing, but the proposed machine learning (ML) task (see [27][28][29][30]) largely exceeds this topic.…”

Section: A Machine Learning Algorithm Extending the Applicability Of ...mentioning

confidence: 99%

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A Machine Learning Algorithm That Experiences the Evolutionary Algorithm’s Predictions—An Application to Optimal Control

Mînzu,

Arama

2024

Mathematics

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Using metaheuristics such as the Evolutionary Algorithm (EA) within control structures is a realistic approach for certain optimal control problems. They often predict the optimal control values over a prediction horizon using a process model (PM). The computational effort sometimes causes the execution time to exceed the sampling period. Our work addresses a new issue: whether a machine learning (ML) algorithm could “learn” the optimal behaviour of the couple (EA and PM). A positive answer is given by proposing datasets apprehending this couple’s optimal behaviour and appropriate ML models. Following a design procedure, a number of closed-loop simulations will provide the sequences of optimal control and state values, which are collected and aggregated in a data structure. For each sampling period, datasets are extracted from the aggregated data. The ML algorithm experiencing these datasets will produce a set of regression functions. Replacing the EA predictor with the ML model, new simulations are carried out, proving that the state evolution is almost identical. The execution time decreases drastically because the PM’s numerical integrations are totally avoided. The performance index equals the best-known value. In different case studies, the ML models succeeded in capturing the optimal behaviour of the couple (EA and PM) and yielded efficient controllers.

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Section: Discussionmentioning

confidence: 99%

Section: A Machine Learning Algorithm Extending the Applicability Of ...mentioning

confidence: 99%

A Machine Learning Algorithm That Experiences the Evolutionary Algorithm’s Predictions—An Application to Optimal Control

Mînzu,

Arama

2024

Mathematics

View full text Add to dashboard Cite

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“…Extending their applicability is a challenge, involving techniques and control structures diminishing the execution time [19][20][21]. One can consider that our work addresses the execution time's decreasing, but the proposed machine learning (ML) task (see [22][23][24][25]) largely exceeds this topic.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Algorithm That Experiences Evolutionary Algorithm’s Predictions – Application to Optimal Control.

Mînzu,

Arama

2023

Preprint

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Using metaheuristics, such as the Evolutionary Algorithm (EA), within control structures is a realistic approach for certain optimal control problems. Their role is often predicting the optimal control values over a prediction horizon using a process model (PM). The big computational effort sometimes causes problems. Our work addresses a new issue: whether a machine learning (ML) algorithm could “learn” the optimal behaviour of the couple (EA, PM). A positive answer is given by proposing datasets apprehending this couple's optimal behaviour and appropriate ML models. Following a design procedure, a number of closed-loop simulations will provide the sequences of optimal control and state values, which are collected and aggregated in a data structure. For each sampling period, datasets are extracted from the aggregated data. The ML algorithm experiencing these datasets will produce a set of regression functions. Replacing the EA predictor with the ML model, new simulations are carried out, proving that the state evolution is almost identical. The execution time decreases drastically because the PM’s numerical integrations are totally avoided. The performance index equals the best-known value. In different case studies, the ML models succeeded in capturing the optimal behaviour of the couple (EA, PM) and yielded efficient controllers.

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“…The simulation program is executed M times (e.g., two hundred times); the two sequences are collected each time and aggregated into a data structure. This data structure expresses the PSO predictor's experience as a decision maker; it will be used to obtain the ML models [22][23][24][25][26].…”

mentioning

confidence: 99%

Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth

Mînzu,

Arama,

Rusu

2024

Processes

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Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models and implementing the controllers and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior, such that the process evolves almost identically. The resulting controllers’ execution time have decreased hundreds of times while keeping their optimality; the performance index has even slightly increased.

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Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Cited by 3 publications

References 20 publications

A Machine Learning Algorithm That Experiences the Evolutionary Algorithm’s Predictions—An Application to Optimal Control

A Machine Learning Algorithm That Experiences the Evolutionary Algorithm’s Predictions—An Application to Optimal Control

A Machine Learning Algorithm That Experiences Evolutionary Algorithm’s Predictions – Application to Optimal Control.

Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth

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