Tag: healthtech
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Health Insurance Cross Sell Prediction with ML Model Tuning & Validation
The content discusses the use of AI and Machine Learning (ML) for insurance cross-selling. It covers topics such as data preparation, model training with different algorithms, parameter optimization, and model evaluation. The study showcases the ability of ML models (HGBM, XGBoost, Random Forest) to predict cross-sell customers in the insurance sector, providing potential for improved…
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Top Fast-Growing Apps in 2023
The OKTA Business at Work report and blogs by Leon Zucchini discuss the fastest-growing and new app categories. Key trends include the growth of collaboration, communication, and travel apps, and the adoption of multi-cloud. Ten notable growing apps are Kandji, Grammarly, Bob, Notion, Prisma Access, Navan, GitLab, Ironclad, Terraform Cloud, and Figma. Emerging apps include…
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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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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…
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A Roadmap from Data Science to BI via ML
The blog post presents a comprehensive roadmap to Data Science (DS), providing an overview of career prospects, the field’s intersections with Mathematics, Statistics, and Computer Science, and its business relevance. The text details the earning potential of data scientists and the steps towards becoming one, including Data Analysis, Machine Learning, and Business Intelligence. It highlights…
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A Comparison of Binary Classifiers for Enhanced ML/AI Breast Cancer Diagnostics – 1. Scikit-Plot
The post compares binary classifiers in Scikit-Learn using the breast cancer dataset. It includes data analysis, ML preparation, learning curves, feature dominance, calibration curves, confusion matrix, ROC curve, precision-recall curve, KS statistic, cumulative gains, lift curves, PCA, and classification reports. Various models’ performances are compared with focus on key metrics and feature evaluations.
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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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Breast Cancer ML Classification – Logistic Regression vs Gradient Boosting with Hyperparameter Optimization (HPO)
Breast Cancer (BC) is the leading cause of death among women worldwide. The present study optimizes the use of supervised Machine Learning (ML) algorithms for detecting, analyzing, and classifying BC. We compare Logistic Regression (LR) against Gradient Boosting (GB) Classifier within the Hyperparameter Optimization (HPO) loop given by GridSearchCV. We use the publicly available BC dataset…
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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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HealthTech ML/AI Q3 ’22 Round-Up
Featured Photo by Andy Kelly on Unsplash This blog presents a Q3 ’22 summary of current healthtech ML/AI innovation methods, trends and challenges. Virtual reality, artificial intelligence, augmented reality, and machine learning are all healthcare technology trends that are going to play a vital role across the entire healthcare system. Let’s take a look at…
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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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