This work proposes to accurately predict the medical diagnosis by using machine learning algorithms. Patient data analysis helps us to develop models with exact diagnosis predictions, consequently enabling rapid intervention and treatment.
Methodology:
- It requires a dataset with patient records including characteristics including symptoms, medical history, and treatment.
- Multiple stages of data preparation help to prepare the dataset for model These procedures include feature normalisation, missing value addressing, and dataset cleansing.
- Including logistic regression, decision tree, random forest, along with support vector machine among several classification techniques,
- Every model is trained on a dataset and has hyperparameter fine-tuning to achieve the best
- Measuring precision, recall, precision, accuracy, F1-score, and ROC-AUC helps to evaluate the performance of a model.
Predicting medical diagnosis, the Support Vector Machine classifier reached the highest possible degree of accuracy. The study presents an approach for building predictive models that could assist with medical decision-making and shows the capability of machine learning in the domain of healthcare.