International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
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Natural Language Processing and summarization of medical symptomatic data from geographical diverse locations Snehal N. Palve1, R.N Awale 2, Vaibhav Awandekar 3, Sunil Lakdawala 4 1 M.Tech
Student, Electrical Department, Veermata Jijabai Technological Institute, Mumbai, India Professor, Electrical Department, Veermata Jijabai Technological Institute, Mumbai, India 3 Senior R&D Engineer, A3 Remote Monitoring Technologies Pvt Ltd, India 4 Director, A3 Remote Monitoring Technologies Pvt Ltd, India ---------------------------------------------------------------------***--------------------------------------------------------------------is vital for the prevention and treatment of the illness. Hence, Abstract - This Paper deals with detection of regional 2
symptomatic diseases at an earlier stage. This early detection across various geographical locations will be helpful for early diagnosis and for death prevention on a larger scale. Adopting random methods for early detection and its respective factors will be unsystematic. The proposed method is for detecting disease epidemics/pandemics by considering large symptomatic data and Natural Language Processing (NLP). These Symptomatic data is available in comments made by the doctors during physiological data acquisition from the database by hospitals. NLP will be used to find the common diseases over different geographical locations.
detecting the spread of such epidemics / pandemics at an early stage across various locations is going to be helpful for early diagnosis and for death prevention on a bigger scale. After identification of an emerging pandemic, detecting the disease spread, local and international healthcare organizations may be notified earlier in order that they will take steps to halt the disease's progress [2]. Thus, controlling the epidemic diseases at the start of its spread may be a vital solution for epidemics/pandemics.
Key Words: Regional symptomatic disease; natural
Harini D K, Natesh M [3], In this paper, machine learning algorithms is used for effective prediction of diseases. It uses both structured and unstructured data from hospital for effective prediction of diseases .Latent factor model is used to overcome the difficulty of missing data. A new convolutional neural network based multi-modal disease risk prediction (CNN-MDRP) algorithm is proposed in this paper. The proposed algorithm accuracy prediction reaches 94.8% than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithm.
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language processing; machine learning; physiological data acquisition; statistical data analysis.
1. INTRODUCTION Nowadays, healthcare is taken into account to be a significant challenge. Infectious diseases are among the most serious health issues in the world. The emergence of these diseases can be through air, water, direct contact with the infected person, biologically and ecological determinants [1]. In the year 2020, the world has witnessed an outbreak of infectious diseases, which is Corona Virus/ Covid-19. Around 64 L deaths globally are reported till date and it is getting multiplied at an awfully faster rate. Awareness of such infectious diseases needs to be spread widely among the people for the prevention of being infected in prior. These infectious diseases go on spreading over larger areas leading to epidemics and pandemics. Also, these outbreaks have major impacts on the population both socially and economically.
Shratik J. Mishra, Albar M. Vasi , Vinay S. Menon, Prof. K. Jayamalini [4] , The system implemented had the accuracy of 86.67% on the dataset of 120 patient data. The current system covered the general diseases or the more commonly occurring disease, so that early prediction and treatment could be done, and the fatality rate of deadly diseases decreases, with the economic benefit. Minsung Kim, Joon Yeop Lee, Hwangnam Kim [5],This paper presents an Early Warning System (EWS) which is able to predict infectious disease outbreaks and detect the sudden increase of any livestock disease with the potentials to become epidemic before spreading.
In the situation of epidemics/pandemics, when everything is virtual, there are many places in our country which lack medical facilities. The traditional way of treatment to disease may not be enough in the case of serious problems. Developing a medical diagnosis system based on Natural Language Processing (NLP) and Machine Learning (ML) algorithms for prediction of any disease can help in a more accurate diagnosis and preventing the spread for pandemics. Accurate and on-time analysis of any health related problem
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Pahulpreet Singh Kohli, Shriya Arora [6], In this paper, different classification algorithms were applied, each with its own advantage on three separate databases of disease available in UCI repository for disease prediction.
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