Disease Prediction from Symptoms using Machine Learning with Flask App Project

Disease Prediction from Symptoms involves using machine learning (ML) techniques to identify and compare medical cases based on clinical features such as symptoms, diagnoses, test results, and treatment outcomes. This approach helps healthcare professionals find similar cases, aiding in diagnosis, treatment recommendations, and patient management.

Data Collection:
Medical records, including structured (e.g., lab results) and unstructured data (e.g., clinical notes).

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.

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.