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- W4362501114 abstract "The prevalence of non-communicable diseases (NCDs) is a significant global issue. This study tries to estimate the population's shared risk factors for NCDs. The general public who are unaware of the risk factors for NCDs, including high blood pressure, strenuous activity, smoking habits, and many more, can benefit from this. Data from rural areas, where people are less knowledgeable about such disorders, have been used. Spark will be used to perform preprocessing on this unstructured input to create structured data. The structured data that will be kept in the database will be analyzed using the machine learning algorithm. Dashboards will be used to display the analytical results. If some metrics exceed a certain limit based on analytics, the likelihood of contracting the disease will be forecasted. According to analytics, if any metrics exceed the then particular limit then risk for the disease can be anticipated." @default.
- W4362501114 created "2023-04-05" @default.
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- W4362501114 creator A5048511461 @default.
- W4362501114 date "2022-12-23" @default.
- W4362501114 modified "2023-09-30" @default.
- W4362501114 title "Analytics on Risk Factors Correlated to Non-Communicable Diseases using Machine Learning" @default.
- W4362501114 doi "https://doi.org/10.1109/ccet56606.2022.10080674" @default.
- W4362501114 hasPublicationYear "2022" @default.
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