RETRACTED ARTICLE: HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues

Mutha, Rakhi; Lavate, Santosh; Limkar, Suresh; Khedkar, Ganesh; Ashok, Wankhede Vishal

doi:10.1007/s00500-023-08311-9

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Based on the review of existing correlation models, it can be observed that defining disease-level correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this section proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis (Mutha et al 2023 ) via heterogeneous correlative body organ analysis. As per Fig.…”

Section: Design Of An Augmented Bioinspired Deep Learning-based Multi...mentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

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Section: Design Of An Augmented Bioinspired Deep Learning-based Multi...mentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

Self Cite

View full text Add to dashboard Cite

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RETRACTED ARTICLE: MPCITL: design of an efficient multimodal engine for Pre-emptive identification of CKD via incremental transfer learning on clinical data samples

Mutha

Pawar²,

Limkar³

et al. 2023

Soft Comput

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RETRACTED ARTICLE: HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues

Cited by 2 publications

References 34 publications

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

RETRACTED ARTICLE: MPCITL: design of an efficient multimodal engine for Pre-emptive identification of CKD via incremental transfer learning on clinical data samples

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