Journal: Drug Invention Today

Article Id: JPRS-PS-00002206
Title: Risk factors, susceptibility, and machine learning techniques for cancer prediction
Category: Pharmacological Screening
Section: Review Article
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    Machine learning (ML) based techniques are being widely applied in the field of cancer prediction. Predictive analytics applied to health care has a huge scope in this particular domain. Data from the real world pertaining to cancerous cases have the abundant potential for enabling the realization of better performing predictive techniques since timely diagnosis and prediction of the advanced stage is extremely important for cancer detection and recovery. However, incorporation of correct and appropriate features is extremely important for accurate prediction models. This paper gives an account of the recent trends in ML techniques applied in the field of cancer detection. The various techniques reported have been compared in terms of the features selected and the performance parameters. Various databases used in different researches have also been reported. The major shortcomings found in the reported approaches have also been highlighted. In view of the same, the importance of considering various miscellaneous features and creation of a comprehensive, automated, and data-driven prediction tool has been discussed in the future scope. Significance statement: This study gives a comprehensive survey of the risk factors pertaining to different forms of cancer. It also highlights the importance of utilizing predictive data analytics to extract knowledge from these factors for early prognosis. This study will thus help the researchers to uncover the risk factors as predictors to contraction of cancer. Thus, as a specific region-based study, a new paradigm in understanding the quality of life and predicting it for better assessment of cancer treatment and its after effects can be arrived at.

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    Author(s) Name:

    Smita Jhajharia1 , Seema Verma2 , Rajesh Kumar3

    Affiliation(s) Name:

    1 Department of Computer Engineering, Banasthali University, Jaipur, Rajasthan, India,

    2 Department of Electronic and Computer Engineering, Banasthali University Jaipur, Rajasthan, India,

    3 Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    *Corresponding author: Smita Jhajharia, Department of Computer Engineering, Banasthali University, Jaipur-304022, Rajasthan, India.

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    Smita Jhajharia1 , Seema Verma2 , Rajesh Kumar3

    Title:Risk factors, susceptibility, and machine learning techniques for cancer prediction
    Journal:Drug Invention Today
    Vol(issue):10 (April)
    Page No: (580-592)
  • Experimental Methods Keywords

    Methodology:Feature, Machine learning, Predictive analytics, Prognosis
    Research Materials:Cancer


Cancer Feature Machine learning Predictive analytics Prognosis

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