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ai-integration-healthcare

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This repository is included artificial intelligence, machine learning, data science, computer vision projects related to healthcare.

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# AI-Projects-for-Healthcare

This repository is included artificial intelligence, machine learning, data science, computer vision projects related to healthcare.

Information about completion: ✅(Complete), 🚧 (Work in Progress), ❌ (Incomplete)

## Table of Contents

- [AI-Projects-for-Healthcare](#ai-projects-for-healthcare)
  - [Table of Contents](#table-of-contents)
  - [Projects in Bootcamp Education](#projects-in-bootcamp-education)
  - [Projects in Healthcare + Artificial Intelligence Education](#projects-in-healthcare--artificial-intelligence-education)
  - [Research Projects](#research-projects)

## Projects in Bootcamp Education

- **[Breast Cancer Classification ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/master/Breast%20Cancer%20Classification/%20Breast_Cancer_Classification.ipynb):** The aim of this project is classification the tumors into malignant or benign with machine learning techniques.
- **[Detecting COVID-19 with Chest X-ray using PyTorch ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blo/731619a7f8e041059d15832d56c1ca1df540a221/Detecting%20COVID-19%20with%20Chest%20X-Ray%20using%20PyTorch/Detecting%20COVID-19%20with%20Chest%20X-Ray%20using%20PyTorch.ipynb):** The aim of this project is detection
  COVID-19 on the chest x-ray images by using PyTorch. The [dataset](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database) is taken from Kaggle.
- **[Diabetes Prediction with PySpark MLLIB ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/731619a7f8e041059d15832d56c1ca1df540a221/Diabetes%20Prediction%20With%20Pyspark%20MLLIB/Diabetes_Prediction.ipynb):** The aim of this project is to build logistic regression model using PySpark MLLIB
  to classify patients as either diabetic or non-diabetic. This project is a Guided project([Link](https://www.coursera.org/projects/diabetes-prediction-with-pyspark-mllib)) available on Coursera.
- **[Heart Failure Data Analysis ✅](https://jovian.ai/edaaydinea/health-failure-prediction):** The aim of this project is to make a detailed exploratory data analysis on the Heart Failure Prediction [dataset](https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data) which is taken from Kaggle by using the plotly library.
- **[Relationship between COVID-17 & Happiness in that Country ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/master/Relationship%20between%20COVID-19%20%20%26%20Happiness%20in%20that%20Country/covid19%20data%20analysis%20notebook.ipynb):** The aim of this project is to work on whether
  where is any relationship between the spread of the coronavirus in a country and how happy people are living in
  that country or not. The dataset is taken from COVID-19 dataset published by Johns Hopkins University and World
  Happiness Report.

## Projects in Healthcare + Artificial Intelligence Education

- **[DNA Classification Project ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/master/DNA%20Classification%20Project/DNA%20Classification.ipynb):** The aim of this project is to find out whether the DNA sequence is the promoter.
- **[Heart Disease Classification Project ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/master/Heart%20Disease%20Classification%20Project/Heart%20Disease%20Classification.ipynb):** The aim of this project is to predict  the condition of her/his disease throughout a classification algorithm based on a neural network. The dataset is taken from [UCI Machine learning Respiratory](https://archive.ics.uci.edu/ml/datasets/Heart+Disease).
- **[Diagnosing Coronary Artery Disease Project ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/master/Diagnosing%20Coronary%20Artery%20Disease%20Project/Diagnosing%20Coronary%20Artery%20Disease.ipynb):** The aim of this project is to predict the condition of her/his disease throughout a classification algorithm based on a neural network.
- **[Breast Cancer Detection ✅](https://github.com/edaaydinea/AI-Projects-for-Healthcare/blob/master/Breast%20Cancer%20Detection/Breast_Cancer_Detection.ipynb):** The aim of this project is to predict the breast cancer from a digitized image of a fine needle aspirate (FNA) of a breast muscles

## Research Projects

- The projects here are included in different repository.
- **[Chest X-Ray Image Classification by using PyTorch, CNN (Pneumonia)✅](https://github.com/edaaydinea/Chest-Xray-Image-Classification-by-using-PyTorch-CNN):** This project involves building a convolutional neural network (CNN) using PyTorch to classify chest X-ray images from a Kaggle dataset to detect pneumonia in pediatric patients.
- **[Pneumonia Detection on Chest X-ray Images with Deep Learning (Keras) ✅](https://github.com/edaaydinea/Pneumonia-Detection-on-Chest-Xray-Images-with-Deep-Leaning):** This project focuses on detecting pneumonia from chest X-ray images using deep learning techniques, likely involving Convolutional Neural Networks (CNNs) built with a framework like Keras or TensorFlow, and includes steps like data preprocessing, model training, and evaluation.
- **[Estimating the Probability of Confirmed COVID-19 Cases Taking into the Intensive Care Unit (ICU) ✅](<https://github.com/edaaydinea/Estimating-the-Probability-of-Confirmed-COVID-19-Cases-Taking-into-the-Intensive-Care-Unit-ICU>):** This project aims to estimate the probability of confirmed COVID-19 patients requiring Intensive Care Unit (ICU) admission using machine learning or statistical modeling based on available data.
- **[OP1 - Prediction of the Different Progressive Levels of Alzheimer's Disease ✅](<https://github.com/edaaydinea/OP1-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease>):** This project involves developing models to predict the different progressive levels of Alzheimer's disease (AD) using a real dataset and various machine learning techniques.
- **[OP2 - Prediction of the Different Progressive Levels of Alzheimer's Disease with MRI data ✅](<https://github.com/edaaydinea/OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data>):** This project focuses on **predicting the different progressive levels of Alzheimer's disease using MRI data** through various machine learning models and techniques.
- **[Low Grade Glioma Segmentation ✅](<https://github.com/edaaydinea/Low-Grade-Glioma-Segmentation>):**This project aims to segment low-grade gliomas from medical images, likely brain MRIs, using deep learning or image processing techniques.
- **[Multiple Sclerosis Lesion Segmentation from Brain Magnetic Resonance Images via Fully Convolutional Neural Network 🚧](<https://github.com/edaaydinea/Multiple-Sclerosis-Lesion-Segmentation-from-Brain-Magnetic-Resonance-Images-via-Fully-Convolutional>)**
- **[Magnetic Resonance Imaging Comparisons of Demented and Non-demented Adults 🚧](<https://github.com/edaaydinea/Magnetic-Resonance-Imaging-Comparisons-of-Demented-and-Non-demented-Adults>)**
- **[Optimization of Human Sensory Neuron Differentiation for Pain Research✅](https://github.com/edaaydinea/LifeArc_BiologyResearch)**: This project, undertaken within LifeArc, focuses on developing and optimizing an in vitro model system of human sensory neurons. The primary goal is to create a reliable and reproducible platform for testing novel analgesic compounds by directly converting human skin fibroblasts into functional sensory neurons.
- **[Distinguishing Cognitive Reading States using Advanced NLP and Transformers ✅](https://github.com/edaaydinea/LLMAnalysisCognitiveStates):** This project explores the application of Natural Language Processing (NLP) and Large Language Model (LLM) techniques to differentiate between cognitive states, specifically Normal Reading (NR) and Task-Specific Reading (TSR). The analysis is performed on a subset of the ZuCo dataset, focusing on linguistic characteristics of sentences read under these two conditions. The project progresses from baseline machine learning models with traditional NLP features to advanced techniques involving fine-tuned transformer embeddings and more complex neural network architectures.

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