An efficient stacking based NSGA-II approach for predicting type 2 diabetes

Patil, Ratna; Rawandale, Shitalkumar; Rawandale, Nirmalkumar; Rawandale, Ujjwala S.; Patil, Shrishti

doi:10.11591/ijece.v13i1.pp1015-1023

Cited by 7 publications

(13 citation statements)

References 10 publications

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“…Many individuals diagnosed with diabetes are unaware of the risk aspects they may be exposed to before the diagnosis happens [25]. Patil et al [29] introduced an approach for predicting Type-2 Diabetes Mellitus (T2DM) utilizing a stacking ensemble model. The primary aim is to minimize the period between diabetes disease detection and medical checkups.…”

Section: Related Workmentioning

confidence: 99%

“…It outperforms other ensemble models in prediction performance, so we have chosen to apply it in our study. The stacking approach aims to provide us with the concept of meta-learning, which can minimize ML model generalization errors [29].…”

Section: A Stackingmentioning

confidence: 99%

“…Furthermore, the model excels at extracting relevant temporal features, which improves its capacity to detect significant patterns in time series data. This skill is essential for making good forecasts in time-series forecasting jobs [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: E Sequential Dense Layers (Sdls)mentioning

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: Related Workmentioning

confidence: 99%

Section: A Stackingmentioning

confidence: 99%

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Integrated Ensemble Model for Diabetes Mellitus Detection

Alzubaidi,

Halawani,

Jarrah

2024

IJACSA

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“…Standardization involves rescaling the features such that they have the properties of a standard normal distribution with a mean of zero and a standard deviation of one. The standard scaler is defined by the mathematical equation [54], [55], [56], [57], [58], [59], [60] given in equation (1).…”

Section: B Data Preprocessingmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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“…This process aims to unveil the underlying relationships between the attributes, representing the environmental parameters, and the class labels, which signify the various disease possibilities. By thoroughly understanding the dataset's structure and the connections between its elements, we equip ourselves with the knowledge required to make informed decisions and predictions [20], [26].…”

mentioning

confidence: 99%

Virtual analysis of machine learning models for diseases prediction in muskmelon

Kannan,

Amutha,

Dasarathan

et al. 2024

IJEECS

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Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.

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An efficient stacking based NSGA-II approach for predicting type 2 diabetes

Cited by 7 publications

References 10 publications

Integrated Ensemble Model for Diabetes Mellitus Detection

Integrated Ensemble Model for Diabetes Mellitus Detection

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Virtual analysis of machine learning models for diseases prediction in muskmelon

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