Detecting Diabetes in Patients
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Updated
Jul 28, 2022 - Jupyter Notebook
Detecting Diabetes in Patients
🔨 [UNDER DEVELOPMENT] A broad intro into scientific and mathematical computations in Python for data analysis, with recipes and short tutorials. Specific interests in pathophsyiology, comp bio, expression analysis, ethnography, clinical data, and GIS (for disparities research). Curated by @milodubois.
This project helps to predict the diabetes of a patient by analysing their no.of pregnencies, glucose level, Blood pressure, Skin thickness, Insulin, Body mass index, Diabetes pedigree function and age of the patient. I have trained the data using RandomForestClassifier and also I have integrated it with a webpage using STREAMLIT.
This repository contains Python code for performing diabetes classification using two machine learning algorithms: K-Nearest Neighbors (KNN) and Logistic Regression. The code also includes a comparison of the models' performance.
In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.
A software tool that uses machine learning techniques to predict whether a person has diabetes based on their medical data.
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exploring data that looks at how certain diagnostic factors affect the diabetes outcome of women patients.
Analysis of self-care/ lifestyle habits and their relation with diabetes diagnosis explored. Final visualisation on Tableau public.
Predicting Diabetes using multiple machine learning algorithms and find out which has the most predictive ablility for this dataset.
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Diabetes prediction using KNN-Classifier algorithm. Step by step guided notebook
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Integration improving the lives of diabetics with MyFitnessPal & Nightscout.
Learning with sklearn diabetes and iris flower dataset, single and multiple linear regression, classification with multi-layer perceptron, kneighbors and support vector machines.
Android/iOS app for Diabetes monitoring and prediction. (UI-based features & Predictive Analysis using Deep Learning)
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