Tag: public health
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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…
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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…
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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…
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Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)
The project utilizes AI-driven cardiovascular medicine with a focus on early diagnosis of heart disease using Artificial Neural Networks (ANN). Aiming to improve early detection of heart issues, the project processed a dataset of 303 patients using Python libraries and conducted extensive exploratory data analysis. A Sequential ANN model was subsequently built, revealing excellent performance…
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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…
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Comparison of Global Growth Stocks – 2. AZN
Summary: A comprehensive QC assessment of top growth stocks in Q1’23 was conducted, focusing on A-rated AstraZeneca PLC (AZN) in the biopharmaceutical industry. The company’s financial indicators, technical analysis, and algorithmic trading signals were analyzed. Backtesting showed a 34% profit from investing in AZN, outperforming the benchmark by 50%.
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COVID-19 Data Visualization, Impact and Vaccine Sentiment Analysis
The coronavirus COVID-19 pandemic is the defining global health crisis of our time and the greatest challenge we have faced since World War Two. After over two years of living with Covid-19, we are learning to adapt to a world with this disease. 2022 ends with looming risk of a new coronavirus variant, health experts…
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50 Coronavirus COVID-19 Free APIs
The COVID-19 pandemic has triggered the creation of numerous free, accessible APIs providing real-time data on the virus’s spread. With data in JSON and CSV formats sourced from reputable institutions, developers and researchers can track cases, government policies, and more for informed decision-making and analysis.
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99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow
Breast cancer is a significant global health concern, affecting 12% of women. Machine Learning and Artificial Intelligence techniques play a crucial role in early diagnosis using image features. The study demonstrates a successful Neural Network model for breast cancer classification, achieving 98% accuracy and 98% F1-score. Multiple metrics confirm the model’s efficiency.
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ML/AI Breast Cancer Diagnosis with 98% Confidence
We demonstrate the importance of hyperparameter optimization (HPO) for enhancing ML prediction accuracy. Specifically, we will focus on the Random Forest Classifier (RFC) as an ensemble of decision trees. RFC is a supervised ML algorithm that has been applied successfully to the BC binary classification.
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A Comparative Analysis of Breast Cancer ML/AI Binary Classifications
This study is dedicated to #BreastCancerAwarenessMonth2022 #breastcancer #BreastCancerDay @Breastcancerorg @BCAction @BCRFcure @NBCF @LivingBeyondBC @breastcancer @TheBreastCancer @thepinkribbon @BreastCancerNow. One of the most common cancer types is breast cancer (BC), and early diagnosis is the most important thing in its treatment. Recent studies have shown that BC can be accurately predicted and diagnosed using machine learning (ML) technology. Our…
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A Comparison of ML/AI Diabetes-2 Binary Classification Algorithms
The post discusses the increasing urgency to diagnose and treat Type-2 Diabetes (T2D), particularly in developing nations. It delves into the use of data-driven techniques, including ML/AI, in processing T2D data. Different ML/AI methods including DNN, SVM, and DT are applied to the Kaggle PIMA Indian Diabetes (PID) dataset, and performance is assessed using Python…
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HPO-Tuned ML Diabetes-2 Prediction
The blog details the author’s tests with machine learning (ML) for enhanced prediction of Type-2 diabetes. The workflows used include RandomizedSearchCV HPO, accuracy metrics, and cross-validation. The post provides in-depth analysis and comparisons of different ML algorithms, such as RandomForestClassifier, GaussianNB, and DecisionTreeClassifier. The experiment findings indicate that RandomForestClassifier with RandomizedSearchCV provides the highest accuracy,…
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AI-Guided Drug Recommendation
AI-Guided Drug Recommendation in Python using NLP text processing. Key steps: WordCount images, NLP Pre-Processing, NER via spacy, LDA topic modelling, and Word2Vec Vectorization for reviews using pretrained glove model. Input data: the Kaggle UCI ML Drug Review dataset. Applications in the pharmaceutical industry, including drug R&D, drug repurposing, improving pharmaceutical productivity, and clinical trials,…
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Drug Review Data Analytics
UCI Drag Review Data Analytics An AI assisted medication recommender framework is truly vital with the goal that it can assist specialists and help patients to build their knowledge of drugs on specific health conditions. We Build a Drug Recommendation System that recommends the most effective drug for a certain condition based on available reviews…
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AI-Driven Skin Cancer Diagnosis
Using TensorFlow library in Python, we can implement an image recognition skin cancer classifier that tries to distinguish between benign (nevus and seborrheic keratosis) and malignant (melanoma) skin diseases from only photographic 2D RGB images
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The Application of ML/AI in Diabetes
The study uses machine learning (ML) to predict diabetes in patients. Classifying diabetics is complex, but ML can offer quick and accurate predictions. The study focuses on type 2 diabetes and uses the Pima Indians database for diagnostic measurements. Models were trained with Python and the Anaconda library. Feature engineering and exploratory data analysis revealed…
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Firsthand Data Visualization in R: Examples
The post discusses the power of R, a programming language and software environment used for data analysis and graphic representation. It provides examples on using various R packages like ggplot2 for data visualization and tidycensus for downloading geographic and demographic data. It also explains how to create different types of plots such as choropleth maps,…
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