Diabetes Prediction in Healthcare at Early Stage Using Machine Learning Approach

Hassan, Md. Mehedi; Peya, Zahrul Jannat; Mollick, Swarnali; Billah, Md. Al-Mamun; Shakil, Md. Mehadi Hasan; Dulla, Asaf Ud

doi:10.1109/icccnt51525.2021.9579869

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…XGBoost, a gradient boosting learning model, has been widely used for analyzing medical data for classification and prediction in healthcare. It has achieved accurate prediction in hypertension outcomes [30], diabetes [31], cardiovascular [32] and coronary heart diseases [33]. Compared with deep learning models, the biggest advantage is that it has faster speed and stronger robustness when processing large-scale datasets [34].…”

Section: Related Workmentioning

confidence: 99%

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Li,

Du,

et al. 2024

BMC Med Inform Decis Mak

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Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. Correct prediction of the occurrence of HE in advance is critical to help the doctors to determine the next step medical treatment. Most existing studies focus only on the occurrence of HE within 6 h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6 h but within 24 h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24 h, as well as the occurrence of HE every 6 h within 24 h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24 h. In this study, to solve the issue of highly imbalanced data set, which is a frequent case in medical data analysis, we used the SMOTE algorithm for data augmentation. To evaluate our method, we used a data set consisting of 582 patients records, and compared the results of proposed method as well as few machine learning methods. Our experiments show that XGBoost achieved the best prediction performance on the balanced dataset processed by the SMOTE algorithm with an accuracy of 0.82 and F1-score of 0.82. Moreover, our proposed method predicts the occurrence of HE within 6, 12, 18 and 24 h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24 h can be predicted accurately by the proposed method.

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Section: Related Workmentioning

confidence: 99%

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Li,

Du,

et al. 2024

BMC Med Inform Decis Mak

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“…While some studies highlight the efficacy of targeted approaches, there is a paucity of research that systematically investigates the application of multivariate data classification methods in optimizing positive response rates [7].This paper aims to bridge this gap by providing a nuanced understanding of the evolving dynamics in bank marketing. Through a synthesis of existing literature, we identify the need for a paradigm shift in campaign strategies and propose an exploration of multivariate data classification methods as a solution [9]. The subsequent sections of this paper delve into the methodology, economic backdrop, and implications of adopting a datadriven approach in the domain of bank marketing [1].…”

Section: Iiliterature Reviewmentioning

confidence: 99%

Customer Deposit Forecasting - Optimizing Deposit Predictions through CRM and Machine Learning Integration

Sagar

2023

IJSREM

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This machine learning project revolves around predicting customer deposits within a banking context. The banking sector heavily relies on revenue generated from long-term deposits by customers. Understanding customer characteristics is crucial for banks to enhance product sales, leading to the employment of marketing strategies aimed at target customers. The advent of data- driven decisions has prompted the utilization of data analysis, feature selection, and machine learning techniques to analyze customer characteristics and predict their decisions accurately. Leveraging a comprehensive dataset encompassing customer demographics, financial histories, and interaction records, the project employs thorough exploratory data analysis (EDA) and preprocessing techniques to unveil patterns and relationships. Categorical features like job, marital status, and education are scrutinized, providing valuable insights. Preprocessing steps include encoding categorical variables, addressing outliers, and handling imbalances in the dataset. The dataset is partitioned into training and testing sets, and machine learning models such as Random Forest Classifier and XG Boost Classifier are employed for prediction, optimized through GridSearchCV. Cross-validation scores reveal the models' efficacy, with Random Forest and XGBoost demonstrating promising performance. This project underscores the significance of meticulous data analysis and preprocessing in constructing accurate predictive models within the banking domain. The identified strengths of our model suggest its potential application in optimizing customer deposit forecasts. This contribution advances the field of financial predictive modeling by introducing a robust solution tailored for accurate customer deposit prediction, thereby facilitating informed decision-making in the banking industry. Keywords:-Random Forest Classifier, XGB Classifier, GridSearch CV, XGBoost

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“…Except for one upstream location, all forecast results have shown low water quality. Here, the number of hidden layers (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), hidden layer neurons (5,10,15,20,25), transfer, training, and learning functions were used to train and verify the neural network model through 12 inputs and one output. Their study has revealed that an artificial neural network with eight hidden layers and 15 hidden neurons accurately predicted the WQI with an accuracy of 0.93.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because random forests work with subsets of data, they are faster than decision trees. So, we can easily solve hundreds of features without any complication [16]. Figure 6 depicts the random forest algorithm's flow.…”

Section: Random Forestmentioning

confidence: 99%

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

Hassan¹,

Hassan²,

Akter³

et al. 2021

HCIS

Self Cite

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The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model's feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality,

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Diabetes Prediction in Healthcare at Early Stage Using Machine Learning Approach

Cited by 17 publications

References 15 publications

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Customer Deposit Forecasting - Optimizing Deposit Predictions through CRM and Machine Learning Integration

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

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