Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis

R. Kavitha; Preeti Naval; Murli Manohar Gour; Manish Kaushik

doi:10.1007/978-981-97-8043-3_111

Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis

R. Kavitha
, Preeti Naval
, Murli Manohar Gour
, Manish Kaushik

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

Resumen

Gaussian method fashions (GPMs) are typically used to investigate complex physiologic statistics for the cause of identifying patterns and predicting outcomes of disorder states. In this study, 14-day pre-operative facts from 73 patients with white-blood-cellular-negative spontaneous pleural effusions were used to optimize the capacity of GPMs to predict postoperative pulmonary effusion formation. The statistics contained scientific measures (pre-operative temperature, albumin degrees, radiographic features (pleural flocculation, and so on.), and echocardiography measures (right atria length, etc.) as input variables for the GPMs. Through optimization of hyper parameters, pre-processing techniques, and characteristic choice algorithms, the performance of the GPMs was advanced drastically, with an AUC price that passed 0.95.

Idioma original	Inglés estadounidense
Título de la publicación alojada	Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023
Editores	Amit Kumar, Vinit Kumar Gunjan, Sabrina Senatore, Yu-Chen Hu
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	711-716
-	6
ISBN (versión impresa)	9789819780426
DOI	https://doi.org/10.1007/978-981-97-8043-3_111
Estado	Indizado - 2025
Publicado de forma externa	Sí
Evento	5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023 - Hyderabad, India Duración: 15 dic. 2023 → 16 dic. 2023

Serie de la publicación

Nombre	Lecture Notes in Electrical Engineering
Volumen	1274 LNEE
ISSN (versión impresa)	1876-1100
ISSN (versión digital)	1876-1119

Conferencia

Conferencia	5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023
País/Territorio	India
Ciudad	Hyderabad
Período	15/12/23 → 16/12/23

Nota bibliográfica

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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10.1007/978-981-97-8043-3_111

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Kavitha, R., Naval, P., Gour, M. M., & Kaushik, M. (2025). Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis. En A. Kumar, V. K. Gunjan, S. Senatore, & Y.-C. Hu (Eds.), Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023 (pp. 711-716). (Lecture Notes in Electrical Engineering; Vol. 1274 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-8043-3_111

Kavitha, R. ; Naval, Preeti ; Gour, Murli Manohar et al. / Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis. Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023. editor / Amit Kumar ; Vinit Kumar Gunjan ; Sabrina Senatore ; Yu-Chen Hu. Springer Science and Business Media Deutschland GmbH, 2025. pp. 711-716 (Lecture Notes in Electrical Engineering).

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title = "Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis",

abstract = "Gaussian method fashions (GPMs) are typically used to investigate complex physiologic statistics for the cause of identifying patterns and predicting outcomes of disorder states. In this study, 14-day pre-operative facts from 73 patients with white-blood-cellular-negative spontaneous pleural effusions were used to optimize the capacity of GPMs to predict postoperative pulmonary effusion formation. The statistics contained scientific measures (pre-operative temperature, albumin degrees, radiographic features (pleural flocculation, and so on.), and echocardiography measures (right atria length, etc.) as input variables for the GPMs. Through optimization of hyper parameters, pre-processing techniques, and characteristic choice algorithms, the performance of the GPMs was advanced drastically, with an AUC price that passed 0.95.",

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author = "R. Kavitha and Preeti Naval and Gour, \{Murli Manohar\} and Manish Kaushik",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023 ; Conference date: 15-12-2023 Through 16-12-2023",

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Kavitha, R, Naval, P, Gour, MM & Kaushik, M 2025, Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis. En A Kumar, VK Gunjan, S Senatore & Y-C Hu (eds.), Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023. Lecture Notes in Electrical Engineering, vol. 1274 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 711-716, 5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023, Hyderabad, India, 15/12/23. https://doi.org/10.1007/978-981-97-8043-3_111

Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis. / Kavitha, R.; Naval, Preeti; Gour, Murli Manohar et al.
Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023. ed. / Amit Kumar; Vinit Kumar Gunjan; Sabrina Senatore; Yu-Chen Hu. Springer Science and Business Media Deutschland GmbH, 2025. p. 711-716 (Lecture Notes in Electrical Engineering; Vol. 1274 LNEE).

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis

AU - Kavitha, R.

AU - Naval, Preeti

AU - Gour, Murli Manohar

AU - Kaushik, Manish

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Gaussian method fashions (GPMs) are typically used to investigate complex physiologic statistics for the cause of identifying patterns and predicting outcomes of disorder states. In this study, 14-day pre-operative facts from 73 patients with white-blood-cellular-negative spontaneous pleural effusions were used to optimize the capacity of GPMs to predict postoperative pulmonary effusion formation. The statistics contained scientific measures (pre-operative temperature, albumin degrees, radiographic features (pleural flocculation, and so on.), and echocardiography measures (right atria length, etc.) as input variables for the GPMs. Through optimization of hyper parameters, pre-processing techniques, and characteristic choice algorithms, the performance of the GPMs was advanced drastically, with an AUC price that passed 0.95.

AB - Gaussian method fashions (GPMs) are typically used to investigate complex physiologic statistics for the cause of identifying patterns and predicting outcomes of disorder states. In this study, 14-day pre-operative facts from 73 patients with white-blood-cellular-negative spontaneous pleural effusions were used to optimize the capacity of GPMs to predict postoperative pulmonary effusion formation. The statistics contained scientific measures (pre-operative temperature, albumin degrees, radiographic features (pleural flocculation, and so on.), and echocardiography measures (right atria length, etc.) as input variables for the GPMs. Through optimization of hyper parameters, pre-processing techniques, and characteristic choice algorithms, the performance of the GPMs was advanced drastically, with an AUC price that passed 0.95.

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ER -

Kavitha R, Naval P, Gour MM, Kaushik M. Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis. En Kumar A, Gunjan VK, Senatore S, Hu YC, editores, Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023. Springer Science and Business Media Deutschland GmbH. 2025. p. 711-716. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-97-8043-3_111

Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis

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