Comparison and Analysis of Various Machine Learning Algorithms for Disease Prediction

People nowadays suffer from a variety of diseases because of environmental factors and their lifestyle choices. As a result, disease prediction at an earlier stage becomes a critical task. Machine learning is increasingly being used in medical diagnosis. Using machine learning in disease prediction would make it easier for doctors to treat patients. However, supervised machine learning (ML) algorithms have shown significant promise in outperforming standard systems for disease diagnosis and assisting medical experts in the early detection of high-risk diseases. Having these prediction systems works as an additional hand for the doctors in order to identify the disease more accurately in less time. The challenges involved in this research are the accuracy issues that is the predicted disease might be differing every single time even though the input symptoms given are the same. Also, another challenge is that the model could not handle big data properly. To overcome these challenges, we have used different algorithms and obtained the result for every algorithm individually. The algorithm with highest accuracy gives the output for that user and is the best algorithm for the problem statement.

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