Machine learning project analyzing the U.S. Chronic Disease Indicators dataset. Includes data preprocessing, EDA, and four trained models—Logistic Regression, KNN, Decision Tree, and Random Forest—with evaluation metrics and visualizations for classification

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making.

This project is based on Data-mining and Machine Learning technique using Python and Scikit-learn. It is used to predict Chronic Kidney Disease of Patients.

This repository contain code of Chronic Kidney Disease Detection Prediction Project. The goal of this project is predict the chronic kidney disease using parameters like specific gravity, Red Blood count, Hemoglobin, Hyper tension etc.. The machine learning algorithm random forest algorithm is used with hyperparameter tuning which is having 97.5% accuracy.

This project aims to develop an embedded system that monitors chronic disease patients who need frequent medical check-ups.

Machine Learning Web App Built Using Flask Deployed on Heroku

In this project,Chronic Kidney Disease dataset (CKD dataset) in UCI Machine learning repository have been explored, which includes health parameters of 400 patients with 24 features ( excluding the target label class ).

R scripts for my Master of Public Health project at the University of Glasgow: 'Estimating the Effects of Physical Activity and Physical Fitness on Chronic Obstructive Pulmonary Disease Development: A UK Biobank Study'

Through this project, ONC in partnership with National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), advanced the application of AI/ML in patient-centered outcomes research (PCOR) by generating high quality training datasets for a chronic kidney disease (CKD) use case – predicting mortality within the first 90 days of dialysis.

A deep learning project that uses stacked ensemble approach to detect and predict Chronic Kidney disease

Glaucoma Detection and Classification using Deep Learning Glaucoma is a condition of eye in which optic nerve is damaged due to abnormally high pressure in the eye. It is a chronic and irreversible disease. It is one of the leading cause of blindness across the globe in people over the age of 60. There is no cure for glaucoma, but early detection and medical treatment can prevent from disease progression. A goal of this project was to use deep learning architecture to build a model to detect and classify glaucoma by combining multiple deep features. Keras was used to build the model. We used publicly available database Drishti-GS1. Methodology: This project was divided into two parts: Glaucoma Detection First, ROI (Region of interest) which is an area where optic disc and cup are located in the center and blood vessels of the Glaucoma fundus images were extracted using U shape convolutional neural network and then cup to disc ratio was calculated to classify if the image was glaucomatous or normal. This Paper was used for ROI extraction and disc segmentation. Glaucoma Classification Cup to disc ratio was used for glaucoma classification. VGG16 CNN model was used to distinguish between glaucoma and non-glaucoma related images from fundus images. Glaucoma severity can also be classified from cup to disc ratio: Mild ( CDR >0.3 and <0.5) Moderate (CDR >=0.5 and <0.8) Severe (CDR >=0.8)

Working space for a Shiny Dashboard displaying Chronic Disease Registry data, in collaboration with a UBC MDS Capstone project team (May - June 2022)

Data mining projects include predicting risk score of chronic diseases with NHANES data and analysis of patient and insurance claim data.

This project is a full-stack machine learning web app designed to predict Chronic Kidney Disease (CKD) from patient data. Built with Flask, scikit-learn, and a clean front-end, it offers real-time predictions through a user-friendly interface.

This project is about the development of a machine learning model and website for the automated diagnosis of chronic kidney disease from blood tests using machine learning.

Medical App Based on Deep Learning with Medical Watch Based on IOT [ Dr Care ] •The main objective of this project is to Health Care. - Doctor Care is an application to help patients in their healthy condition as it benefits them in more than one way, such as communicating with doctors in an easy way and other advantages. - The specific objective of this project is :- • Doctor Care able to examine images such as (Brain Tumor – Chest x-ray - Skin Cancer, Heartbeat, Retinal OCT). • Medical Watch is the part of hardware can measure (oxygen ratio - heartbeat - temperature), the application read these measurement and sends an alarm when there's a sudden malfunction in the health condition of the elderly based on IOT. • Doctor Care will provide us with Chat bot to detect if a person has a certain disease by talking with the chat bot, or also know information about a specific disease or treatment for a disease • Doctor Care will provide us with making posts about diseases where the patient talks about what hurts him and someone gives advice to him, interaction with posts. • Doctor Care will give us information about where the hospital to us in the map section and the application displays the expected time to reach the hospital if a particular mode of transport is used or on foot. • The patient can contact to the doctor online. • Doctor Care allows you to browse medical news when online or offline. • Doctor Care allows you to log in and out as a (patient or Doctor) • Doctor Care also provides us with setting alarms to follow up on taking medications and this helps in treating chronic conditions such as diabetes, heart disease, etc. - Used Tools :- • Android. • Arduino. • Deep Learning. • Firebase. • Fast API. • Adobe XD. • Kaggle. • Azure. • Heroku. .

慢性病大数据分析处理

The Chronic Kidney Disease Predictor is a machine learning project that offers early detection, accurate prediction, and risk assessment of chronic kidney disease. It utilizes patient data analysis, provides a user-friendly interface, and serves as a valuable decision support tool for healthcare professionals.

This project presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD).

This project is used to predict the chronic kidney disease , I have used decision trees as algorithm as it best suits the model

This project proposes creating a low-cost, IoT-based remote patient monitoring system to address growing healthcare strains caused by chronic diseases and an aging population.

This project aims to explore clinical and laboratory features associated with chronic kidney disease (CKD) and to identify key predictors that distinguish CKD from non-CKD individuals.

In this Project we aim to dive into a Present Societal Pandemic issue which we are facing around us past 2 years due to outbreak of Novel Corona Virus.  Getting tested for covid-19 virus is not an easy deal with costly RT-PCR test, and delayed results, and with its no. of variants with different mutations emerging everyday all the new methods found to detect the virus and its variant have either become : - Ineffective as all tests may not find each of the variants. - Each of them a set a finical restrictions for the technology used. - Each test has its own detection time .  Chest X-Ray already exists and overcomes most of the above drawbacks, but still fail to give long term effects or severity.  So as a Solution We Aim to develop a model to give large no. of classifications and comparisons of Covid Patients and whether it leads to pneumonia disease , also these models could be trained to classify long term effects after years of infection how things could change w.r.t chest infections, and lead to other chronic disease.

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3rd year project about a medical web app. I am responsible for requirement gathering, system analysis, system design, and documentation of the web app

This project is an AI-powered Predictive Healthcare System that helps identify individuals at risk of developing chronic diseases such as diabetes, heart disease, or obesity. The system provides personalized recommendations for preventive care based on lifestyle and medical data.

A machine learning project that predicts Chronic Kidney Disease using patient medical data. The system applies data preprocessing, feature encoding, and classification models in Python to support early disease detection and healthcare decision-making.

This project seeks to predict whether person has chronic kidney disease (ckd) or no chronic kidney disease (notckd)?

MSC Data Science Final project on Chronic kidney disease