PML Micro Project Disease Prediction

PML Micro Project Disease Prediction. Santhosh S 225229133 1 MSc Data Science.

Outline. Background and Motivation. Existing Methodology.

Background and Motivation. Disease prediction is an important area of research that aims to identify the risk of a person developing a particular disease. Early identification of such risks can facilitate timely intervention and lead to improved patient outcomes.

Existing methodology. They already used Random forest Classifier for this disease prediction..

Proposed methodology. We used Support Vector Classifier (SVC) for the disease prediction..

Evaluation. Precision Recall F1-score Accuracy 0.98 Macro average 0.99 0.99 0.98 Weighted average 0.99 0.98 0.98.

Model archive in Github. https://github.com/Santhosh-33/Disease-Predictio.

Referances. https://www.kaggle.com/code/ghaidalthobaity/disease-prediction-rand.

Conclusion. In conclusion, the disease prediction project using Support Vector Machine (SVM) classifier is an effective approach to predicting diseases. The project uses a large dataset to train the model and achieve high accuracy in disease prediction. The SVM classifier is a powerful machine learning algorithm that can handle complex datasets and provide accurate predictions..

Thank you.