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@article{158969,
        author = {Sitesh Choudhary and Neelam Malyadri and HARSHVARDHAN SINGHAL and SANCHIT RAJ},
        title = {Disease Prediction using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {187-192},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158969},
        abstract = {For the purpose of preventing and treating disease, an accurate and speedy assessment of any health-related problem is vital. If the issue is serious, the conventional diagnostic procedure might not be enough. The creation of a machine learning (ML)-based medical diagnosis system for illness prediction can lead to a diagnosis that is more accurate than the traditional approach. We have created a system for predicting diseases using several ML algorithms. Around 230 diseases may be processed using the data. The diagnosis algorithm produces the ailment that the patient may be experiencing based on their symptoms, age, and gender. The user's disease is predicted by a "Disease Prediction" system based on predictive modelling using the symptoms they provide as input. The technology calculates the probability that the disease will manifest by analyzing the user's symptoms as input.},
        keywords = {Predictive Modeling, Naïve Bayes Classifier, Disease prediction, machine learning, symptoms},
        month = {},
        }