Disease Prediction Based on Individual’s Medical History Using CNN

Krishnamoorthy, Monica; Hameed, Mohammed Sharafath Abdul; Kopinski, Thomas; Schwung, Andreas

doi:10.1109/icmla52953.2021.00022

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Optuna, introduced in 2019 by Akiba et al, is a free hyperparameter tuning framework designed to streamline the trial-and-error process in optimizing model training accuracy [42][43]. It employs a targeted API-based strategy, allowing the automatic optimization of hyperparameter values for various machine learning algorithms within a specified trial limit.…”

Section: G Optunamentioning

confidence: 99%

“…It employs a targeted API-based strategy, allowing the automatic optimization of hyperparameter values for various machine learning algorithms within a specified trial limit. Versatile and 'pythonic' in operation, Optuna makes no distinction between machine learning and deep learning frameworks [42][43]. In this research, Optuna was utilized to tune hyperparameters such as dense layers units, batch size, activation function, loss function and others.…”

Section: G Optunamentioning

confidence: 99%

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Integrated Ensemble Model for Diabetes Mellitus Detection

Alzubaidi,

Halawani,

Jarrah

2024

IJACSA

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Diabetes Mellitus, commonly referred to as (DM), is a chronic illness that affects populations worldwide, leading to more complications such as renal failure, visual impairment, and cardiovascular disease, thus significantly compromising the individual's well-being of life. Detecting DM at an early stage is both challenging and a critical procedure for healthcare professionals, given that delayed diagnosis can result to difficulties in managing the progression of the disease. This study seeks to introduce an innovative stacking ensemble model for early DM detection, utilizing an ensemble of machine learning and deep learning models. Our proposed stacking model integrates multiple prediction learners, including Random Forest (RF), Convolutional Neural Network (CNN) with Long Short-Term Memory networks (CNN-LSTM), and Sequential Dense Layers (SDLs) as base learner models, with the Extreme Gradient Boosting model (XGBoost) serving as the Meta-Learner model. Findings demonstrate that our proposed model achieves a 99% accuracy on the Pima dataset and 97% accuracy on the DPD dataset in detecting diabetes mellitus disease. In conclusion, our model holds promise for developing a diagnostic tool for DM disease, and it is recommended to conduct further testing on the types of diabetes mellitus to enhance and evaluate its performance comprehensively.

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Section: G Optunamentioning

confidence: 99%

Section: G Optunamentioning

confidence: 99%

Integrated Ensemble Model for Diabetes Mellitus Detection

Alzubaidi,

Halawani,

Jarrah

2024

IJACSA

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The Use of Machine Learning Models with Optuna in Disease Prediction

Lai,

Lin,

Liu

et al. 2024

Electronics

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Effectively and equitably allocating medical resources, particularly for minority groups, is a critical issue that warrants further investigation in rural hospitals. Machine learning techniques have gained significant traction and demonstrated strong performance across various fields in recent years. The determination of hyperparameters significantly influences the performance of machine learning models. Thus, this study employs Optuna, a framework specifically designed for optimizing the hyperparameters of machine learning models. Building on prior research, machine learning models with Optuna (MLOPTA) are introduced to forecast diseases of indigenous patients. The numerical results reveal that the designed MLOPTA system can accurately capture the occurrences of specified diseases. Therefore, the MLOPTA system offers a promising approach for disease forecasting. The disease forecasting results can serve as crucial references for allocating hospital resources.

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Disease Prediction Based on Individual’s Medical History Using CNN

Cited by 2 publications

References 21 publications

Integrated Ensemble Model for Diabetes Mellitus Detection

Integrated Ensemble Model for Diabetes Mellitus Detection

The Use of Machine Learning Models with Optuna in Disease Prediction

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