Chronic diseases monitoring and diagnosis system based on features selection and machine learning predictive models

El_Rahman, Sahar A.; Alluhaidan, Ala Saleh; AlRashed, Reem A.; AlZunaytan, Duna N.

doi:10.1007/s00500-022-07130-8

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…The enhanced results of the CKDD-HGSODL approach can be assured by a comparative results analysis, as given in Table 4 and Fig. 14 [15][16]. The outputs depicted the weak outputs of the NN-GA and EP-CRD models.…”

Section: Performance Valiationmentioning

confidence: 89%

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Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

2024

IJIES

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Chronic Kidney Disease (CKD) is a progressive condition that may cause kidney failure, so earlier diagnosis is critical for proper management. The condition has a large fatality, particularly in developing nations. CKD often remains unnoticed because there is no obvious earlier-stage symptom. Meanwhile, earlier diagnosis and on-time clinical intervention are essential for reducing the disease progression. Detecting CKD using deep learning (DL) methods and feature selection (FS) could be a useful application of artificial intelligence (AI) in healthcare. DL algorithms can provide cost-effective and efficient computer-aided diagnoses (CAD) to assist clinicians in accomplishing earlier CKD recognition. Therefore, this study develops an automated CKD detection using Henry Gas Optimization Algorithm with DL (CKDD-HGSODL) approach. The drive of the CKDD-HGSODL approach is to classify and detect the presence of CKD utilizing FS and hyperparameter tuning strategies. In the presented CKDD-HGSODL technique, min-max scaling can be used to normalize the input data. In addition, the CKDD-HGSODL technique utilizes the HGSO model for selecting optimal features. For the CKD detection process, an attention-based gated recurrent unit (AGRU) model can be utilized. At last, the slime mould algorithm (SMA) can be utilized for the optimal hyperparameter selection of the AGRU approach that aids in improving the classifier results. To validate the outcome of the CKDD-HGSODL approach, a comprehensive simulation value is made on the benchmark CKD database. The obtained performances depict the enhanced detection results of the CKDD-HGSODL approach on CKD diagnosis.

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Section: Performance Valiationmentioning

confidence: 89%

“…In [16], introduced a new cloud and IoT-based CKD identification method named Flower Pollination Algorithm (FPA)-based DNN approach (FPA-DNN). This method exploits Oppositional Crow Search (OCS) approach for FS that chooses the optimum feature subsets from the pre-processed data.…”

Section: Related Workmentioning

confidence: 99%

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

2024

IJIES

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“…The GRF is a novel tree-based spatial machine-learning model [42,43]. It has the advantage of not presupposing local linearity and often outperforms an aspatial random forest model in predictive performance [44], but at the cost of greater computational complexity [45].…”

Section: Introductionmentioning

confidence: 99%

Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Lotfata

Georganos

Kalogirou³

et al. 2022

IJGI

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Some studies have established relationships between neighborhood conditions and health. However, they neither evaluate the relative importance of neighborhood components in increasing obesity nor, more crucially, how these neighborhood factors vary geographically. We use the geographical random forest to analyze each factor’s spatial variation and contribution to explaining tract-level obesity prevalence in Chicago, Illinois, United States. According to our findings, the geographical random forest outperforms the typically used nonspatial random forest model in terms of the out-of-bag prediction accuracy. In the Chicago tracts, poverty is the most important factor, whereas biking is the least important. Crime is the most critical factor in explaining obesity prevalence in Chicago’s south suburbs while poverty appears to be the most important predictor in the city’s south. For policy planning and evidence-based decision-making, our results suggest that social and ecological patterns of neighborhood characteristics are associated with obesity prevalence. Consequently, interventions should be devised and implemented based on local circumstances rather than generic notions of prevention strategies and healthcare barriers that apply to Chicago.

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Synergizing Enhanced Neuro-Fuzzy System for Predictive Livestock Healthcare in Cloud IoT Environment

Vigneswari,

Sahapudeen,

Geetha

et al. 2024

2024 11th International Conference on Computing for Sustainable Global Development (INDIACom)

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Chronic diseases monitoring and diagnosis system based on features selection and machine learning predictive models

Cited by 3 publications

References 49 publications

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Synergizing Enhanced Neuro-Fuzzy System for Predictive Livestock Healthcare in Cloud IoT Environment

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