• Disease detection using machine learning is revolutionizing the healthcare industry by enabling the development of systems that can automatically identify diseases with high accuracy. With access to vast amounts of medical data, including patient records, medical images, and diagnostic reports, machine learning (ML) models can learn to detect patterns associated with various diseases. This capability is particularly valuable in early detection, where timely diagnosis can significantly improve patient outcomes. Machine learning models help healthcare professionals make more accurate, faster, and data-driven decisions in areas such as radiology, pathology, and genomics.Python is a leading programming language in this domain due to its powerful libraries for data processing, machine learning, and deep learning. Final-year projects in disease detection using machine learning give students the opportunity to work on real-world healthcare challenges, leveraging data-driven techniques to diagnose diseases like cancer, diabetes, heart disease, and infectious diseases.

Software Tools and Technologies

• • Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
• • Development Tools: Anaconda3 / Spyder 5.0 / Jupyter Notebook
• • Language Version: Python 3.11.1
• • Python ML Libraries: Scikit-Learn / Numpy / Pandas / Matplotlib / Seaborn.
• • Deep Learning Frameworks: Keras / TensorFlow / PyTorch.

List Of Final Year Python Machine Learning Projects in Disease Detection

• COVID-19 Detection from Chest X-Ray Images Using Python
  Project Description : This project applies convolutional neural networks (CNNs) in Python to classify chest X-ray images for detecting COVID-19, enabling faster and more accurate preliminary screening.
• Diabetes Prediction Using Python Machine Learning
  Project Description : This project uses machine learning algorithms such as Logistic Regression, Random Forest, and SVM in Python to predict the likelihood of diabetes based on health indicators like BMI, blood pressure, and glucose levels.
• Brain Tumor Detection from MRI Images Using Python
  Project Description : This project develops a deep learning model in Python to automatically classify MRI brain scans into tumor and non-tumor categories, helping doctors in early diagnosis.
• Heart Disease Prediction Using Python ML
  Project Description : This project creates a Python-based predictive model using patient health data (cholesterol, ECG, blood pressure) to estimate the probability of heart disease, supporting preventive healthcare.
• Parkinson’s Disease Detection Using Python
  Project Description : This project uses Python ML models on biomedical voice measurements to detect Parkinson’s disease at an early stage by identifying irregularities in speech patterns.
• Skin Cancer Detection Using Deep Learning in Python
  Project Description : This project applies CNN-based deep learning in Python to classify dermoscopic skin images into benign and malignant categories, improving early skin cancer detection rates.
• Alzheimer’s Disease Detection Using Python
  Project Description : This project uses Python ML and MRI scan data to classify different stages of Alzheimer’s disease, supporting neurologists in early and accurate diagnosis.
• Breast Cancer Detection Using Python ML
  Project Description : This project applies supervised learning algorithms in Python on the Breast Cancer Wisconsin dataset to predict malignant or benign tumors with high accuracy.
• Tuberculosis Detection from Chest X-Rays in Python
  Project Description : This project builds a Python-based CNN model to detect tuberculosis by analyzing chest X-ray images, assisting in large-scale automated TB screening.
• Liver Disease Prediction Using Python
  Project Description : This project develops a Python machine learning system that uses patient medical records to predict the presence of liver disease, helping in early intervention and treatment.
• Federated Learning for Privacy-Preserving Disease Detection in Python
  Project Description : This project implements federated learning in Python to allow multiple hospitals to collaboratively train disease detection models without sharing sensitive patient data. It ensures privacy while improving predictive accuracy for conditions like cancer, diabetes, and cardiovascular diseases.
• Multimodal Disease Detection Using Python
  Project Description : This project combines multiple data modalities such as medical images, lab test results, and wearable device data to detect diseases. Python ML/DL models fuse these inputs to enhance detection accuracy for complex conditions like neurodegenerative disorders.
• Explainable AI for Disease Diagnosis in Python
  Project Description : This project integrates explainable AI (XAI) methods like SHAP and LIME with Python-based disease detection models to provide transparency in predictions, allowing doctors to understand the reasoning behind model outputs.
• Real-Time Disease Detection from Wearable IoT Data Using Python
  Project Description : This project uses Python and ML algorithms to process real-time data from wearable sensors (heart rate, oxygen saturation, blood pressure) to detect early signs of diseases like arrhythmia, hypoxia, or hypertension.
• Deep Learning-Based Early Cancer Detection in Python
  Project Description : This project applies CNNs and transfer learning in Python to detect cancer at early stages using histopathology and radiology images, improving patient prognosis and treatment outcomes.
• Reinforcement Learning for Adaptive Treatment and Disease Monitoring
  Project Description : This project uses Python-based reinforcement learning to recommend adaptive treatment plans by continuously analyzing patient response data, helping manage chronic diseases like diabetes or cancer.
• AI-Powered Multi-Disease Detection from X-Ray and CT Images
  Project Description : This project builds Python ML/DL models capable of detecting multiple diseases (e.g., pneumonia, tuberculosis, lung cancer) from chest X-ray and CT scan images, enabling comprehensive automated screening.
• Predictive Modeling for Neurodegenerative Diseases Using Python
  Project Description : This project develops Python-based ML models to predict diseases like Parkinson’s or Alzheimer’s using patient movement data, cognitive test scores, and neuroimaging, supporting early interventions.
• GAN-Based Synthetic Data Generation for Disease Detection
  Project Description : This project uses Generative Adversarial Networks (GANs) in Python to create synthetic medical datasets for rare diseases, augmenting training data for ML models and improving detection accuracy.
• Adversarial Attack-Resistant Disease Detection Models in Python
  Project Description : This project develops robust Python-based disease detection models that are resistant to adversarial attacks on medical data, ensuring reliable diagnosis in real-world clinical environments.