Prediction Accuracy Improvement on Disease Risk and Cost Prediction Model

Yu, Shuang; Matsumori, Masaki; Lin, XiaoLie

doi:10.1109/iseeie55684.2022.00025

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A grid search is a technique that adopts the idea of class enumeration, which finds the optimal parameters by comparing all results within a specified parameter range. However, undoubtedly, this will significantly increase search time, and accuracy is also limited by parameters [ 19 ]. A more popular parameter search method is the meta-heuristic algorithm, introduced by Glover [ 24 ] in the tabu search (TS) algorithm in 1989.…”

Section: Cs-ga-xgboostmentioning

confidence: 99%

“…In addition, Yu et al introduced the GA-XGBoost method to disease risk and cost prediction and showed that GA-XGBoost could improve prediction accuracy [ 18 ]. Although GA has a fast convergence rate that can help find the optimal hyperparameters faster and more accurately, it is prone to mature [ 19 ]. Zhang et al introduced CS into XGBoost in 2022 for an insulator pollution degree detection problem and found it could improve classification accuracy [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, this paper proposes a modeling method of CS-GA-XGBoost, integrating GA’s crossover operator into CS, making full use of the advantages of CS’s strong global search ability and GA’s fast rate of convergence [ 19 , 21 ], and applying it to XGBoost’s optimal parameter search, to achieve a more rapid rate of convergence and optimization ability. This paper validates the effectiveness of the proposed CS-GA-XGBoost using measured data of the 2.5-GHz-GaN class-E PA and 2.2–6.5-GHz CMOS PA under three different temperatures (−40 °C, 25 °C, and 125 °C).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Wang,

Zhou

2023

Micromachines

View full text Add to dashboard Cite

Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA’s crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (−40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.

show abstract

Section: Cs-ga-xgboostmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Wang,

Zhou

2023

Micromachines

View full text Add to dashboard Cite

show abstract

Advancing Healthcare Predictions: Harnessing Machine Learning for Accurate Health Index Prognosis

Bhaskar,

Kumar Burramsetty,

Pinnaka

et al. 2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

This study investigates how machine learning (ML) techniques may be used to forecast health indicators' accuracy, which is important for efficient medical monitoring and diagnosis. Numerous machine learning techniques, such as Support Vector Machines and Random Forest, are evaluated by using a heterogeneous dataset that includes vital signs, lab findings, and patient information. Model performance is optimised by careful preprocessing and feature engineering, which includes managing missing variables and normalisation. Model accuracy is further improved via hyperparameter tuning strategies, which are measured using metrics like precision and recall. The findings show that machine learning (ML) models can accurately predict health index accuracy, which may help with early illness identification and individualised treatment plans. The study highlights the potential of machine learning in healthcare decision-making and provides guidance for raising the standard of patient care. Future projects could look into adding more functionality and integrating real-time data for.

show abstract

Prediction Accuracy Improvement on Disease Risk and Cost Prediction Model

Cited by 2 publications

References 0 publications

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Advancing Healthcare Predictions: Harnessing Machine Learning for Accurate Health Index Prognosis

Contact Info

Product

Resources

About