A Comparative Analysis Of Machine Learning Techniques For Human Immunity Level Prediction

The early detection of disease can be crucial as well as challenging. Due to immune diseases our immune system get damaged and it cause harmful to our body. For predicting human immunity levels, the study brings up the combination of ML techniques with the help of the data set with 20,853 entries (rows) and 33 columns (features). For bridging AI and Healthcare together we use Supervised and Unsupervised Learning techniques. Also this study applies Cross-Validation, Regularization. pruning, and early stopping to ensure that the models don’t overfit, which is the key to challenge towards AI and Healthcare. Among these Linear Regression was the best model for this specific dataset and could predict immunity levels accurately . The usage of MSE tells us how much error this model makes in predicting immunity levels. R² tells about the model fitting helps in making useful predictions.