Tag: AIHealth

  • Low-Code AutoEDA of Dutch eHealth Data in Python

    Low-Code AutoEDA of Dutch eHealth Data in Python

    The article details the usage of Python’s Low-Code AutoEDA for examining Dutch Healthcare Authority’s eHealth data. Utilizing various Python libraries like D-Tale, SweetViz, etc., the study aims to understand the healthcare data’s key features to ready it for AI techniques. The motivations include the Dutch government’s support for digital healthcare applications, especially amidst the recent…

  • Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons

    Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons

    Table of Contents Embed Socials: ECG Autoencoder Let’s set the working directory YOURPATH import osos.chdir(‘YOURPATH’)os. getcwd() and import the following libraries import tensorflow as tfimport matplotlib.pyplot as pltimport numpy as npimport pandas as pd from tensorflow.keras import layers, lossesfrom sklearn.model_selection import train_test_splitfrom tensorflow.keras.models import Model Let’s read the input dataset df = pd.read_csv(‘ecg.csv’, header=None) Let’s…

  • Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction

    Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction

    This post discusses the prediction of stroke using machine learning (ML) models, focusing on the use of early warning systems and data balancing techniques to manage the highly imbalanced stroke data. It includes a detailed exploration of the torch artificial neural network training and performance evaluation, as well as the implementation and evaluation of various…

  • 90% ACC Diabetes-2 ML Binary Classifier

    90% ACC Diabetes-2 ML Binary Classifier

    A study aims to develop an ML-driven e-diagnosis system for detecting and classifying Type 2 Diabetes as an IoMT application. By leveraging advanced supervised ML algorithms, the system can predict a person’s diabetes risk based on several factors, provide a preliminary diagnosis, and relay doctor’s guidance on diet, exercise, and blood glucose testing. The Pima…

  • The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    AI Health is leveraging Machine Learning (ML) and Artificial Intelligence (AI) for early diagnosis and prediction of breast cancer (BC), utilizing different ML techniques for binary classification of the disease. A comparative analysis demonstrated that Linear Regression was the most effective classifier based on various performance metrics. This research aims to integrate ML in public…