pcos

There are 0 repository under pcos topic.

• CodecPie / tiny11-mirror

  Your Tiny11 Mirror. Without any registration and or slow Archive.org Servers.

  amd64arm64debloatdebloateddownloadeuropeoperatingsystemospcpcosusawinwindowswindows11windowslight
  82

• GaneshkumarKarunanidhi / PCOS-Detection

  PCOS Detection using DeepLearning

  deep-learningimagenetpcospcos-detectiontensorflow
  Language:Jupyter Notebook 9

• smritig19 / PCOS_Diagnosis_System

  data-visualizationmachine-learning-algorithmspandasnumpymatplotlibseabornscikitlearn-machine-learningknn-classifiersvc-modellogistic-regression-classifierrandom-forest-classifierdecision-tree-classifierxgboost-modelcatboost-modellightgbmmlxtendexploratory-data-analysispredictiondata-preprocessingpcos
  Language:Jupyter Notebook 8

• jyotidabass / PCOS-APP

  A project dedicated to PCOS , which is so common diseases yet not know. September is the PCOS awareness month and with this project I tried to create some awareness.

  heroku-deploymentmachine-learningpcos
  Language:HTML 5

• sakshi170920 / Pcos-App

  First-ever all in one app for women diagnosed with PCOS!

  pcoswomenpcos-reversalhealthchatbotflutterfirebase-databasefirebase-auth
  Language:Dart 5

• Shivangi1Raghav / PCOSprediction

  Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact etiology of the disease. The exact histology of PCOS is not yet clear. It is therefore a multifaceted study, which shares genetic and environmental factors. The aim of this project is to analyse simple factors (height, weight, lifestyle changes, etc.) and complex (imbalances of bio hormones and chemicals such as insulin, vitamin D, etc.) factors that contribute to the development of the disease. The data we used for our project was published in Kaggle, written by Prasoon Kottarathil, called Polycystic ovary syndrome (PCOS) in 2020. This database contains records of 543 PCOS patients tested on the basis of 40 parameters. For this, we have used Machine Learning techniques such as Logistic Regression, Decision Trees, SVMs, Random Forests, etc, A detailed analysis of all the items made using graphs and programs and prediction using Machine Learning Models helped us to identify the most important indicators for the same.

  random-forestmachine-learningmatplotlibseabornfeature-selectionchi-square-testinformation-gainpcos
  Language:Python 4

• Fayyez / PCOS-Detection-Model

  a high-precision model as a cost-effective alternative for the early detection of PCOS, assisting medical professionals without relying on more invasive methods.

  colab-notebookmachine-learningmatplotlibpcospcos-detectionpythonsklearn
  Language:Jupyter Notebook 3

• ranmerc / pcos-prediction-backend

  PCOS Prediction API

  fastapimachine-learningmachine-learning-apipcospcos-detectionpython3svm-model
  Language:Python 3

• skinan / Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis

  This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

  data-cleaningdata-scienceedited-nearest-neighborsgradient-boostingmachine-learningoversamplingpcospolycystic-ovary-syndromesmotesmote-samplingundersamplingxgboost
  Language:Jupyter Notebook 3

• sowmyamanojna / PCOS-Gene-Expression-Data-Analysis

  This repository contains all material related to the project done as a part of the course Algorithmic Approaches to Computational Biology (CS6024) in the Fall 2020 semester.

  cs6024data-analysisgene-expressioniitmpcos
  Language:Jupyter Notebook 3

• hiyabose / MAITRI

  A project dedicated to PCOS , which is so common diseases yet not know. September is the PCOS awareness month and with this project I tried to create some awareness.

  herokuappmachine-learningpcos
  Language:HTML 1

• Maximvan / AHTAOmicsAnalysisPCOS

  Identification for Key Pathways and Genes in Polycystic Ovary Syndrome (PCOS) using a multi-omics approach, as part of the Applied High Throughput Analysis course at Ghent University

  high-throughput-data-analysishpcomicspcosrghent-universitymethylation-analysismicroarray-data-analysismulti-omicspolycystic-ovary-syndromerna-seq-analysis
  1

• nairvismaya / WeCare-PCOS-perdiction

  A website that predicts PCOS based on optimal and minimal clinical and metabolic parameters. The dataset is obtained from kaggle which is a patient survey of 541 women during consultation and clinical examination.

  flaskmachine-learningpcospython
  Language:Python 1

• Rizwan-Hasan / Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis

  This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

  data-sciencemachine-learningxgboostdata-cleaningsmotegradient-boostingoversamplingundersamplingsmote-samplingpcosedited-nearest-neighborspolycystic-ovary-syndrome
  Language:Jupyter Notebook 1

• shhreyaa / Pcos_analysis

  Explored and compared different algorithms such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest and Naive Bayes for the prediction of PCOS

  decision-treelogistic-regressionnaive-bayes-classifierpcosrandom-forestsupport-vector-machines
  Language:Jupyter Notebook 1

• vin0414 / e-PCOS

  Online Poll Survey and Appointment

  awarenesspcos
  Language:PHP 1

• aastha1840 / PCOS_Prediction

  Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact etiology of the disease. The exact histology of PCOS is not yet clear. It is therefore a multifaceted study, which shares genetic and environmental factors. The aim of this project is to analyse simple factors (height, weight, lifestyle changes, etc.) and complex (imbalances of bio hormones and chemicals such as insulin, vitamin D, etc.) factors that contribute to the development of the disease. The data we used for our project was published in Kaggle, written by Prasoon Kottarathil, called Polycystic ovary syndrome (PCOS) in 2020. This database contains records of 543 PCOS patients tested on the basis of 40 parameters. For this, we have used Machine Learning techniques such as Logistic Regression, Decision Trees, SVMs, Random Forests, etc, A detailed analysis of all the items made using graphs and programs and prediction using Machine Learning Models helped us to identify the most important indicators for the same.

  random-forestmachine-learningpcoschi-square-testfeature-selectioninformation-gain
  Language:Jupyter Notebook 0

• RenukaDevi999 / ContextSpecificModel-PCOS

  Code for Context-specific metabolic network model for PCOS with insulin resistance

  metabolic-modelspcos
  Language:MATLAB 0

• Tintedfireglass / PCOS-ANN

  Algorithmic analysis on PolyCystic Ovarian Syndrome data

  artificial-intelligenceneural-networkspcos
  Language:Python