A Critical Analysis of Various Machine Learning Algorithm to Extract Disease Patterns

Computer Science & Engineering

In this fast-pacing world, people desire to live a luxurious life, and to attain the same, they work tirelessly. This not only changes their food habits but also brings along a drastic change in their lifestyle. They are tenser and tend to develop many modern-day chronic diseases at an early age. Despite all this, many people don’t bother to change their living or take proper medication, which in turn later leads to major threats. In this paper, we will deploy various machine learning algorithms like Regression, Decision tree classification, Random Forest, etc. to study the behavior of various chronic diseases and do a comparative analysis of the obtained results for accurate prediction. For this, we have used 3 disease datasets namely the PIMA Indian Diabetes dataset, Indian Liver Patient dataset, heart disease dataset for analysis. The final objective of the paper is to find which ML algorithm is apt for each of the datasets and what features were taken into consideration to achieve the best possible accuracy. This will not only help the patients to take the medications accordingly but will also help other people in day-to-day life to take proper care of themselves.

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