Machine Learning Toward Infectious Disease Treatment

  • Tulika Bhardwaj
  • Pallavi SomvanshiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


The emergence of infectious diseases poses a serious threat to human and animal health. This is evident from the sudden cases of hospital outbreaks due to the surveillance and evolution of zoonotic pathogens. The main reason is the development of resistance mechanism in infectious pathogens against broad-spectrum drugs. This leads to the mortality rate of 69% due to infectious diseases at global level. Although improvements have been made at next-generation epidemiological study level to combat such issues but shortfall still observed due to the gap between patients and governmental authorities assigned for treatment. Additionally, handling, analyzing and updating of large datasets is time consuming and labor intensive. To overcome such limitations, applied informatics was employed for the sorting of multipart disciplines of research and pathogenesis identification and treatment. To understand the underlying problem, mining of the diagnostic techniques was performed focused to execute the correct disease diagnosis in different symptoms from the patient. Data preprocessing enables improvement quality of data as redundant data requires continuous discrete mining for analysis. Graphical interfaces were utilized for the comparative analysis of the problem in n number of ways by random decisions and tree making processes. Categorization of the single problem into supervised, unsupervised and weakly supervised principles offers a complete set of appropriate outputs directed towards disease treatment. The whole process favors data preprocessing, data mining, and data analysis by employing various machine learning approaches, data interpretation by statistical platforms and data visualization. The complete chapter reviewed challenges, pathway, and opportunities provided by machine learning approaches toward infectious disease treatments.


Support vector machine Fuzzy logic Artificial neural network Infectious disease Machine learning 


Ethics Approval and Consent to Participate

Not Applicable


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of BiotechnologyTERI School of Advanced StudiesNew DelhiIndia

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