Disease Prediction using Machine Learning


  • Mohd. Nadeem Khan Student, Department of Computer Science and Engineering, Integral University Lucknow, INDIA
  • Ankita Srivastava Professor, Department of Computer Science and Engineering, Integral University Lucknow, INDIA




Disease Prediction, Machine Learning, Decision Tree Classifier, Healthcare, Chronic Illnesses, Predictive Model


Based on predictive modelling, a disease prediction system determines the user's illness from the signs they provide as input to the system. The system evaluates the user's symptoms as input and outputs the likelihood that the illness will occur. Utilizing a decision tree classifier, disease prediction is accomplished. The likelihood of the illness is calculated by a decision tree classifier. More and more organisations in the biological and healthcare sectors are turning to big data to aid in early disease discovery and better serve their patients. The development of a system that will allow people to forecast chronic illnesses without having to see a doctor or medical professional for a diagnostic is necessary by watching patient signs and using a variety of machine learning modelling methods, different illnesses can be identified. Text and organised data processing do not follow any standard method. The suggested paradigm would examine both organised and random material. Prediction precision can be increased through machine learning. There is a need to research and develop a system that will allow an end-user to anticipate irreversible illnesses without having to consult a specialist or doctor for a diagnostic. To identify different diseases by analysing patient symptoms using various techniques of Machine Learning Algorithms. There is no proper technique for managing text and structured data. Both organised and unstructured material will be examined by the Suggested algorithm. Artificial Learning will improve prediction accuracy.


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How to Cite

Mohd. Nadeem Khan, & Ankita Srivastava. (2023). Disease Prediction using Machine Learning. International Journal of Engineering and Management Research, 13(3), 275–279. https://doi.org/10.31033/ijemr.13.3.40