| Huang et al. [185] (2023) |
Skin Cancer Classification from ISIC dataset, a public dataset |
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Classification of skin cancer for assisting dermatologists. |
| Hu et al. [186] (2023) |
Skin Cancer Detection from ISIC 2018 dataset, an open source dataset |
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A chatbot for detecting seven types of skin cancer. |
| Soni et al. [187] (2023) |
The model was tested using two freely accessible datasets: WISDM and UCI-HAR. |
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Human Activity Recognition Using Deep Learning in Health care. |
| Kanagala et al. [188] (2023) |
Numerous IoT gadgets create massive amounts of info, which is analyzed in order to obtain cognitive data using data analytics. |
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Efficient digital safety solution for optical information safety for medical applications. |
| Khan et al. [189] (2023) |
Brain tumor Detection from Brats2018, BraTs2019 & BraTs2020, Publicly available dataset |
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an automated method for identifying brain tumors using three publicly available, unrestricted datasets. |
| Dua et al. [190] (2023) |
Most datasets are collected via IMU, GPS, or ECG while most datasets are used to recognize physical activity or daily activities |
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Human Activity Recognition with Wearable Sensors. |
| Wang et al. [191] (2023) |
In FRESH, physiological data are collected from individuals by wearable devices |
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Architecture for collaborative learning in smart medical facilities while protecting confidentiality. |
| Baji et al. [192] (2023) |
An automated brain tumour identification from the whole brain atlas database |
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To improve brain tumour detection method using k-means clustering and local binary pattern technique. |
| Hassan et al. [193] (2023) |
Cleveland heart disease dataset(open access) |
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For heart disease prediction. |
| Doshi et al. [194] (2023) |
Brain tumor detection from BraTS dataset a publicly available dataset of brain tumour. |
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This approach separates the regions of interest in MRI images to minimize dimensionality. |
| Hu et al. [195] (2023) |
Landsat-BSA datastet for burn patient images (Open source) |
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Burn case treatment. |
| Ogundepo et al. [196] (2023) |
Publicly available Cleveland heart disease dataset |
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Heart disease prediction areas. |
| Minda et al. [197] (2023) |
Medical data |
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Forecasting Algorithm for Multiple Diseases Based on the Finest Deep Learning method. |
| Raheja et al. [198] (2023) |
Cloud-centric data |
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For the diagnosis of cardiac disorders, an IoT-enabled, encrypted clinical health care architecture is used. |
| Sengar et al. [199] (2023) |
RFMiD dataset which is publicly available dataset |
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Assists eye specialists for detection of rental diseases. |
| Uzun et al. [200] (2023) |
Dataset from web scrapping from websites that are publicly available |
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Rapid detection of monkeypox to reduce the spread of the virus. |
| Jagadeesha et al. [201] (2023) |
Fitzpatrick skin type (FST) dataset (open access) |
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Skin tone detection for assisting dermatologists. |
| Balaha et al. [202] (2023) |
HAM10K dataset of Melanoma Classification |
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To aid dermatologists for skin cancer diagnosis from skin images. |
| Bordoloi et al. [203] (2023) |
UCI Dermatology dataset (Public) |
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Skin treatment cases related to skin disorder. |
| Dileep et al. [204] (2023) |
Dataset regarding cardiovascular conditions at UCI (Public) and real-time dataset |
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An automated system for heart disease prediction. |
| Suha et al. [205] (2022) |
An accessible database of patient administrative hospital records from the New York State Department of Healthcare. |
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Patient length of stay forecasting through Random forest model to aid the hospital management system for predicting the proper treatment plan for a patient. |
| Kundu et al. [206] (2022) |
Monkeypox detection from an open source dataset available at kaggle. |
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a comparison of deep learning and ML methods for monkeypox viral identification. |
| Rahman et al. [207] (2022) |
An accessible database of patient administrative hospital records from the New York State Department of Healthcare. |
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Predicting the time a patient is hospitalized using a distributed learning approach will help to keep data safe. |
| Suha et al. [208] (2022) |
Kaggle burn patient dataset an open source database. |
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Classification of burn patient images into 3 categories based on severity. |
