Disease Prediction from Symptoms using Machine Learning with Flask App Project

Data Preprocessing:
Cleaning, normalizing, and encoding data into a machine-readable format.

Feature Extraction:
Extracting relevant features like patient demographics, vital signs, lab results, and clinical histories.

Similarity Measurement:
Algorithms such as cosine similarity, Euclidean distance, or more advanced techniques like neural embeddings are used to measure case similarity.

Model Training:
Training ML models (e.g., k-NN, clustering algorithms, deep learning models) to learn patterns from labeled or unlabeled patient data.

Evaluation & Deployment:
Evaluating model performance using metrics like precision, recall, and F1-score, then deploying in a healthcare application.

Applications:

Clinical Decision Support: Recommending diagnoses or treatments based on similar past cases.

Medical Research: Identifying cohorts for clinical trials.

Patient Risk Prediction: Assessing the likelihood of complications by comparing similar patient histories.

Personalized Medicine: Tailoring treatments based on individual case similarities.

Follow the steps to train the model after installing the requirements.

Follow the steps to run the project after installing the requirements and Training the Model.