A novel framework for highly contagious diseases deaths prediction using machine learning techniques

被引:0
|
作者
Hasan S. [1 ]
Siddiqui T. [2 ]
Mustaqeem M. [2 ]
Khan N.A. [3 ]
机构
[1] A.K.T.U., I.T.M College, Aligarh
[2] Department of Computer Science, Aligarh Muslim University (AMU), Aligarh
[3] Faculty of Engineering and Technology, Arunachal University of Studies, Namsai
关键词
COVID-19—Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); IFR—infection fatality rate; SIR—susceptible-infectious removed;
D O I
10.1007/s41870-023-01567-2
中图分类号
学科分类号
摘要
The recent pandemic has shown us how a new pathogen can infect previously infected individuals again and the current data systems do not account for that in the SIR Model. During the pandemic, one of the more significant concerns in developing nations was the survival and livelihoods of the people below the poverty line. As these people lacked the necessary means to endure the long and hard-hitting impact of the pandemic on the economy, it led to an inability to follow the official guidelines due to the eminent need for survival. However, this also had another repercussion: the increase in the spread of the disease caused spikes in the number of cases over time. This has led us to believe that by using the percentage of a country's population living under the poverty line, we may be able to hypothetically calculate the number of deaths in a future pandemic under similar economic and social conditions. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
引用
收藏
页码:2795 / 2802
页数:7
相关论文
共 50 条
  • [1] An analytical method for diseases prediction using machine learning techniques
    Nilashi, Mehrbakhsh
    bin Ibrahim, Othman
    Ahmadi, Hossein
    Shahmoradi, Leila
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2017, 106 : 212 - 223
  • [2] A novel framework for landslide displacement prediction using MT-InSAR and machine learning techniques
    Zhou, Chao
    Cao, Ying
    Gan, Lulu
    Wang, Yue
    Motagh, Mahdi
    Roessner, Sigrid
    Hu, Xie
    Yin, Kunlong
    [J]. ENGINEERING GEOLOGY, 2024, 334
  • [3] A Novel Approach for Fare Prediction Using Machine Learning Techniques
    Khandelwal, Kunal
    Sawarkar, Atharva
    Hira, Swati
    [J]. INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING, 2021, 12 (05): : 602 - 609
  • [4] RETRACTED: A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques (Retracted Article)
    Krishnamoorthi, Raja
    Joshi, Shubham
    Almarzouki, Hatim Z.
    Shukla, Piyush Kumar
    Rizwan, Ali
    Kalpana, C.
    Tiwari, Basant
    [J]. JOURNAL OF HEALTHCARE ENGINEERING, 2022, 2022
  • [5] An Intelligent Heart Disease Prediction Framework Using Machine Learning and Deep Learning Techniques
    Allheeib, Nasser
    Kanwal, Summrina
    Alamri, Sultan
    [J]. INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING, 2023, 19 (01)
  • [6] An empirical framework for defect prediction using machine learning techniques with Android software
    Malhotra, Ruchika
    [J]. APPLIED SOFT COMPUTING, 2016, 49 : 1034 - 1050
  • [7] A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction
    Kumar, Ajay
    Kaur, Kamaldeep
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY INNOVATION, 2024, 14 (01) : 29 - 43
  • [8] Prediction of Neurodegenerative Diseases Based on Gait Signals Using Supervised Machine Learning Techniques
    Aich, Satyabrata
    Choi, Ki-Won
    Pradhan, Pyari Mohan
    Park, Jinse
    Kim, Hee-Cheol
    [J]. ADVANCED SCIENCE LETTERS, 2018, 24 (03) : 1974 - 1978
  • [9] Prediction of hypercholesterolemia using machine learning techniques
    Pooyan Moradifar
    Mohammad Meskarpour Amiri
    [J]. Journal of Diabetes & Metabolic Disorders, 2023, 22 : 255 - 265
  • [10] Bankruptcy Prediction Using Machine Learning Techniques
    Shetty, Shekar
    Musa, Mohamed
    Bredart, Xavier
    [J]. JOURNAL OF RISK AND FINANCIAL MANAGEMENT, 2022, 15 (01)