Lung cancer prediction applies machine learning to classify cases as normal, benign, or malignant using patient data like demographics, symptoms, or imaging. Models like Logistic Regression or CNNs are trained on labeled data and evaluated with metrics like accuracy and AUC-ROC, enabling early detection and timely treatment.

Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

PredictiX is a comprehensive multi-disease prediction system built using the MERN stack and integrated with machine learning models. It accurately predicts lung cancer, breast cancer, diabetes, and heart disease, providing a seamless user experience for health diagnostics.

Bioinformatics Project. Trying to find out the most influential metabolomic biomarkers(from 158) for predicting Lung Cancer. Also, trying to make some predictions about the disease.

This project is a Streamlit-based web application designed to predict the likelihood of various diseases based on user-provided health data. By leveraging machine learning models, the app offers predictions for conditions such as diabetes, heart disease, Parkinson's disease, lung cancer, and hypothyroidism.

Multiple Disease Prediction System: An ML-based tool for early disease detection (Diabetes, Heart, Parkinson’s, Liver, Hepatitis, Lung Cancer, Kidney, Breast Cancer). Uses a Streamlit interface with trained models (.sav, .json) for risk prediction. Includes a Healthcare Chatbot for assistance.

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This project is a Streamlit-based web application designed to predict the likelihood of various diseases based on user-provided health data. By leveraging machine learning models, the app offers predictions for conditions such as diabetes, heart disease, Parkinson's disease, lung cancer, and hypothyroidism.

Lung Cancer Prediction Using deepLearning for Doctor signup, Doctor login, Patient Login, Patient Login, OTP validation Lung disease visualization and Predict Lung disease using TrainML algorithm.

This is an prediction model of disease using Machine learning. In this project they predict diabetes, Heart disease, Parkinsons , Lungs cancer,cirrhosis.

Multi Disease Prediction is a website used for predicting diseases like Diabetes, Heart disease, lung cancer etc... Deployed backend with authentication for website using Django and the prediction are done using Machine learning algorithms.

This repository contains ML models developed during my AICTE edunet with SAP and Microsoft Internship. It includes preprocessed datasets, Google colab Notebooks, and trained .pkl models for disease prediction (Breast Cancer, Diabetes, Fetal Health, Heart & Kidney Disease, Lung Cancer). and Streamlit app code.

A cutting-edge AI-driven platform that predicts diseases like heart disease, lung cancer, Parkinson’s, and thyroid conditions using machine learning models. Built with Flask and Python, this clinical decision support system offers high-accuracy predictions, a user-friendly interface, and future-ready features like EHR integration, mobile support,

During my lung cancer disease prediction project, I explored various machine learning models and achieved the highest accuracy of 94% using the Support Vector Machine (SVM). This project provided valuable insights into data preprocessing, model tuning, and the importance of algorithm selection in predictive analysis.

This dataset consists of 5,000 records with 18 features related to lung cancer risk factors and prediction. It includes demographic information, lifestyle habits, medical history, and symptoms associated with pulmonary disease. The dataset is useful for predictive modeling, risk assessment, and medical research.

lung cancer disease prediction

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The project is based on the prediction of lung cancer using Deep Learning

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This project is created using Decision Tree Classifier Model of ML

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A machine learning system to predict the risk of lung cancer, diabetes, and heart attack based on medical data like age, lifestyle, and diagnostic results. Using algorithms like logistic regression and decision trees, it helps in early detection and prevention. Technologies: Python, Scikit-learn, Pandas, Matplotlib

We have worked on a Multi disease prediction datatset from Kaggle and also because of perfect accuracy from the dataset we used a new Lung cancer dataset to compare and performed SFA on them.

This is a Lung Cancer/Pulmonary Disease prediction model using Random Forest prediction.

Backend for machine learning prediction on heart disease, lung cancer adn diabetes

Multi-disease prediction system for Lung Cancer, Brain Stroke, CKD and Liver Disease using Machine Learning and Django

Autism, Parkinsons Disease, Stroke, Covid-19 Infection, Diabetes, Lung Cancer and Anemia Prediction Models Using AIML