Smart Prediction: Class Centric Focal XG- Boost for Accurate Diabetes Forecasting
,
Abstract
Purpose: Diabetes, resulting from insufficient insulin production or utilization, causes extensive harm to the body. The conventional diagnostic methods are often invasive. The classification of diabetes is essential for effective management. The progression in research and technology has led to additional classification approaches. Machine Learning (ML) algorithms have been deployed for analyzing the huge dataset and classifying diabetes.
Materials and Methods: The classification and the regression of diabetic and non-diabetic are performed using the XGBoost mechanism. On the other hand, the proposed class-centric Focal XG-Boost is applied to elevate the model performance by measuring the similarity among the features. The prediction of the model is based on the classification and regression rates of diabetic and non-diabetic individuals, which are anticipated using applicable and effectual metrics to estimate their working performance.
The dataset used in the Class-Centric Focal XG Boost model is attained using the Arduino Uno Kit. The data collection is done under a sampling rate of 100 Hz. The data are gathered from Bharati Hospital Pathology Laboratories, located in Pune.
Results: The inclusive outcomes of the proposed model with their appropriate Exploratory Data Analysis (EDA) among classification and regression, with the suitable dataset used in the study are exemplified.
Conclusion: The proposed Class-Centric Focal XG Boost model has numerous advantages and is less delicate to the hyperparameters than the conventional XGBoost algorithm. As a part of the real-time application of the Class-Centric Focal XG Boost model, the model can be utilized in other communicable and communicable disease classification and detection.
2- Jason I Chiang et al., "Multimorbidity, mortality, and HbA1c in type 2 diabetes: A cohort study with UK and Taiwanese cohorts." Vol. 17 (No. 5), p. e1003094, (2020).
3- Cyril P Landstra and Eelco JP %J Frontiers in endocrinology de Koning, "COVID-19 and diabetes: understanding the interrelationship and risks for a severe course." Vol. 12p. 649525, (2021).
4- Victor Chang, Jozeene Bailey, Qianwen Ariel Xu, Zhili %J Neural Computing Sun, and Applications, "Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms." Vol. 35 (No. 22), pp. 16157-73, (2023).
5- Nazin Ahmed et al., "Machine learning based diabetes prediction and development of smart web application." Vol. 2pp. 229-41, (2021).
6- Isfafuzzaman Tasin, Tansin Ullah Nabil, Sanjida Islam, and Riasat %J Healthcare Technology Letters Khan, "Diabetes prediction using machine learning and explainable AI techniques." Vol. 10 (No. 1-2), pp. 1-10, (2023).
7- Yusuf Bahri Özçelik, Aytaç %J Fractal Altan, and Fractional, "Overcoming nonlinear dynamics in diabetic retinopathy classification: A robust AI-based model with chaotic swarm intelligence optimization and recurrent long short-term memory." Vol. 7 (No. 8), p. 598, (2023).
8- Anas Bilal, Liucun Zhu, Anan Deng, Huihui Lu, and Ning %J Symmetry Wu, "AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning." Vol. 14 (No. 7), p. 1427, (2022).
9- Laxmi Math, Ruksar %J Multimedia Tools Fatima, and Applications, "Adaptive machine learning classification for diabetic retinopathy." Vol. 80 (No. 4), pp. 5173-86, (2021).
10- Abdulhakim Salum Hassan, I Malaserene, and A Anny %J Int. J. Innov. Technol. Explor. Eng Leema, "Diabetes mellitus prediction using classification techniques." Vol. 9 (No. 5), pp. 2080-84, (2020).
11- Jake A Carter, Christina S Long, Beth P Smith, Thomas L Smith, and George L %J Expert Systems with Applications Donati, "Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes." Vol. 115pp. 245-55, (2019).
12- Huma Naz, Sachin %J Journal of Diabetes Ahuja, and Metabolic Disorders, "Deep learning approach for diabetes prediction using PIMA Indian dataset." Vol. 19pp. 391-403, (2020).
13- R K Nadesh and K %J International Journal of Cognitive Computing in Engineering Arivuselvan, "Type 2: diabetes mellitus prediction using deep neural networks classifier." Vol. 1pp. 55-61, (2020).
14- Amani Yahyaoui, Jawad Rasheed, Akhtar Jamil, and Mirsat Yesiltepe, "A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques."
15- Kamlesh Khunti et al., "The impact of the COVID-19 pandemic on diabetes services: planning for a global recovery." The Lancet Diabetes & Endocrinology, Vol. 10 (No. 12), pp. 890-900, (2022).
16- Munish Bhatia, Simranpreet Kaur, Sandeep K Sood, and Veerawali Behal, "Internet of things-inspired healthcare system for urine-based diabetes prediction." Artificial Intelligence in Medicine, Vol. 107p. 101913, (2020).
17- Boon Feng Wee, Saaveethya Sivakumar, King Hann Lim, Wei Kitt Wong, and Filbert H Juwono, "Diabetes detection based on machine learning and deep learning approaches." Multimedia Tools and Applications, Vol. 83 (No. 8), pp. 24153-85, (2024).
18- Monalisa Panda, Debani Prashad Mishra, Sopa Mousumi Patro, and Surender Reddy Salkuti, "Prediction of diabetes disease using machine learning algorithms." IAES International Journal of Artificial Intelligence, Vol. 11 (No. 1), p. 284, (2022).
19- Saloni Kumari, Deepika Kumar, and Mamta Mittal, "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier." International Journal of Cognitive Computing in Engineering, Vol. 2pp. 40-46, (2021).
20- Nazin Ahmed et al., "Machine learning based diabetes prediction and development of smart web application." International Journal of Cognitive Computing in Engineering, Vol. 2pp. 229-41, (2021).
21- Leila Ismail, Huned Materwala, Maryam Tayefi, Phuong Ngo, and Achim P Karduck, "Type 2 diabetes with artificial intelligence machine learning: methods and evaluation." Archives of Computational Methods in Engineering, Vol. 29 (No. 1), pp. 313-33, (2022).
22- Rung-Ching Chen et al., "An end to end of scalable tree boosting system." Sylwan, Vol. 165 (No. 1), pp. 1-11, (2020).
23- Pooneh Khodabakhsh et al., "Prediction of in-hospital mortality rate in covid-19 patients with diabetes mellitus using machine learning methods." Journal of Diabetes & Metabolic Disorders, Vol. 22 (No. 2), pp. 1177-90, (2023).
24- Serpil Ustebay, Abdurrahman Sarmis, Gulsum Kubra Kaya, and Mark Sujan, "A comparison of machine learning algorithms in predicting COVID-19 prognostics." Internal and Emergency Medicine, Vol. 18 (No. 1), pp. 229-39, (2023).
| Files | ||
| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Class-Centric Focal XG-Boost Minimized Error Diabetes COVID-19 Machine Learning Classification and Regression. | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
