A novel data-driven robust framework based on machine learning and knowledge graph for disease classification

“…In contrast, for unstructured datasets, Social Media data and Search Keyboards are the most frequently used dataset for forecasting disease outbreaks (e.g., Influenza-like illness (ILI)). There are also studies conducted that used multiple sources of data such as Social Media Data, Search Keywords, Meteorological Data ( [21] , [23] , [33] , [37] , [68] , [69] ) and also Epidemiology Data coupled with Demographic Data ( [50] , [55] , [57] ).…”

Section: Reporting Of Review Findingsmentioning

confidence: 99%

“…This threat can be handled by incorporating multiple data obtained from different studies [48] . For instance, incorporating epidemiological data that includes incidence, distribution, and control of diseases and meteorological data from different locations may produce more reliable results [50] . Besides that, several findings have also suggested that incorporating epidemiological, demography and meteorological data may also improve the performance of the forecasting algorithms [46] , [48] .…”

Section: Reporting Of Review Findingsmentioning

confidence: 99%

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

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Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

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“…The learning rate in terms of mean squared error is 0.005. Age, location or time are the factors that influence the impact of the noncommunicable diseases such as cardio vascular disease, cancer, diabetes, hypertension, cholesterol, thyroid etc., and these factors are variable and changes continuously [8]. It has been claimed that 70% of the deaths are caused due to noncommunicable diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Since unstructured data is also considered in classifying the disease, the level of accuracy has been improved. A fusion method RKRE based on ResNet and expert system has been proposed and attained an average correct proportion of 86.95% [8]. The process is divided into four phases labelled as data fusion, feature extraction, machine learning models and expert advice.…”

Section: Introductionmentioning

confidence: 99%

LSTM-RNN Based Approach for Prediction of Dengue Cases in India

Doni¹,

Sasipraba²

2020

ISI

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Data driven health care research is accentuated due to the Gargantua data available in the form of structured and unstructured. Forecasting the impact of infectious and epidemic diseases will be of major assistance to the health care industry. The objective of this work is to study the impact of dengue cases across India by applying the Deep Learning methodologies based on Long Short-Term Memory using Recurrent Neural Networks. The factors considered in the prediction method are climatic conditions, temperature, rainfall data, humidity and population considered for the period between 2014 and 2019. The activation function applied is ReLU and on training the model using LSTM the level of accuracy in forecasting the epidemic is over 89% for infection and for death it is 81%. The Root Mean Square Error values are also computed and it is observed that when the number of iterations is increased the error value decreased. The proposed methodology assists the Health care department to make safety precautions before the outbreak of the dengue fever.

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A novel data-driven robust framework based on machine learning and knowledge graph for disease classification

Cited by 41 publications

References 26 publications

A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

LSTM-RNN Based Approach for Prediction of Dengue Cases in India

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