An Hybrid Ensemble Machine Learning Approach to Predict Type 2 Diabetes Mellitus

Geetha, G.; Prasad, K. Mohana

doi:10.14704/web/v18si02/web18074

Cited by 6 publications

(6 citation statements)

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“…The Random Forest ensemble approach outperformed AdaBoost and Bagging concerning accuracy, scoring 97 percent. Prasad and Geetha [26] propose an ensemble model utilizing ensemble approaches like bootstrap aggregation, RF, and Ada-boost, together with classification techniques such as (Naive Bayes). Joshi et al [27] used the logistic regression model and the decision tree to predict diabetes type-2 in the Pima dataset, with an accuracy reaching 78%.…”

Section: Related Workmentioning

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%

“…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%

Integrated Ensemble Model for Diabetes Mellitus Detection

Alzubaidi,

Halawani,

Jarrah

2024

IJACSA

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“…To calculate the INFLO score, both sets of neighbors are combined. Subsequently, the LOF technique is applied to compute the local reachability score and density [5].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Algorithms for Low-Dimensional and High-Dimensional Data: A Critical Study

Rehman,

Waseem,

Sattar

et al. 2024

PakJET

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Suspicious events or objects that differ from the norm in data can be discovered using anomaly identification. Identifying anomalies is critical for many applicable domains of life, e.g., preventing credit card theft and spotting intrusions into networks. It is possible to detect anomalies on a global scale as well as at the local level. A global outlier is a data point beyond the norm for the entire dataset, while a local outlier may be inside the norm for the entire dataset but outside the surrounding data points. Data outlier identification methods that are performed locally are inadequate. Therefore, better algorithms are required to investigate the high velocity of data and identify local outliers. Machine learning and data mining techniques need to be investigated to determine the pros and cons of anomaly identification residing inside data. The density based LOF method can be applied as the best choice to identify local outliers. While many variants of LOF exist for low-dimensional data, none are suitable for high-dimensional data. This research study discusses LOF, COF, and CBLOF methods for spotting local outliers in low and high-dimensional data. Regarding the size of the dimension, the performance of density-based algorithms is examined based on accuracy and time complexity. In this scenario, CBLOF achieves outstanding results due to its distinctive method of employing cluster-based local outlier detection.

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“…Kumari et al have proposed an ensemble voting classifier that uses the ensemble of three ML algorithms, viz., LR, NB, and RF for the classification considering the evaluative measures like accuracy, precision, recall, and F1-score on PIDD and claimed to have achieved comparatively enhanced results on binary classifications [28]. Geetha and Prasad have built a hybrid model named T2DDP that doctors can effectively use to treat diabetic patients by employing supervised classification algorithms such as NB and ensemble algorithms like bagging with RF and AdaBoost for DT and found that the forecast will be submitted to the patient's cell phone at an early stage to make the immediate decisions about the health risk [29]. Shynu et al have introduced efficient blockchainbased secure healthcare services for disease prediction in fog computing, considering purity, normalized mutual information (NMI), and accuracy as performance evaluators on PIDD and Cleveland heart disease dataset (CHDD) and thereby claimed that the proposed work efficiently clusters and predicts the disease compared to other methods [30].…”

Section: Introductionmentioning

confidence: 99%

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

Pati

Parhi

Pattanayak

et al. 2022

Wireless Communications and Mobile Computing

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Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F -measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key.

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An Hybrid Ensemble Machine Learning Approach to Predict Type 2 Diabetes Mellitus

Cited by 6 publications

References 0 publications

Integrated Ensemble Model for Diabetes Mellitus Detection

Integrated Ensemble Model for Diabetes Mellitus Detection

Anomaly Detection Algorithms for Low-Dimensional and High-Dimensional Data: A Critical Study

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

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