Tag: healthtech

  • Top Fast-Growing Apps in 2023

    Top Fast-Growing Apps in 2023

    This post was inspired by the recent OKTA Business at Work report and the follow-up Medium blogs by Leon Zucchini published in Apr 7 and Apr 24. Table of Contents Popular App Categories 10 Fastest-Growing Apps 10 Hottest New Apps Summary The following trends are set to shake-up the (Mobile) App industry: A few of…

  • 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

    Specifically, we will compare the (1) SMOTE-balanced Torch NN (viz. the Cross-Entropy Adam Optimizer) against the (2) Sinnott’s Python algorithm from scikit-learn to be validated by various scikit-learn metrics, such as AUC, precision, recall, F-measure and accuracy.  Table of Contents Our Jupyter notebook and the entire Python project will be stored in the working directory…

  • Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)

    Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)

    Featured Photo of Karolina Grabowska on Pexels. Data Preparation Let’s set the working directory HEART23 import osos.chdir(‘HEART23’)os. getcwd() and import the libraries import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snssns.set() from scipy.stats import skew from sklearn.preprocessing import StandardScalerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score, roc_curve, roc_auc_score, precision_score, recall_score import scikitplot…

  • 90% ACC Diabetes-2 ML Binary Classifier

    90% ACC Diabetes-2 ML Binary Classifier

    Featured Photo by Nataliya Vaitkevich on Pexels. Acknowledgements Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261–265). IEEE Computer Society Press. Diabetes EDA & Prediction|Acc %90.25 & ROC %96.38 The…

  • Applying a Risk-Aware Portfolio Rebalancing Strategy to ETF, Energy, Pharma, and Aerospace/Defense Stocks in 2023

    Applying a Risk-Aware Portfolio Rebalancing Strategy to ETF, Energy, Pharma, and Aerospace/Defense Stocks in 2023

    In this post, we will apply the Guillen’s asset rebalancing algorithm (cf. the Python code) to the following risk-aware portfolio: stocks = [‘SPY‘, ‘XOM‘, ‘ABBV‘, ‘AZN‘, ‘LMT‘] The initial portfolio value to be allocated is portfolio_value = 10**6 and the weight allocation per asset is weights = [0.15 , 0.30, 0.40, 0.075, 0.075] Conventionally, our…

  • A Comparative Analysis of The 3 Best Global Growth Stocks in Q1’23 – 2. AZN

    A Comparative Analysis of The 3 Best Global Growth Stocks in Q1’23 – 2. AZN

    StockNews TradingView The 1-week summary of AZN based on the most popular technical indicators, such as Moving Averages, Oscillators and Pivots: TradingView Analyst Rating based upon 42 analysts giving stock ratings to AZN in the past 3 months. The 37 analysts offering 1 year price forecast for AZN. AlgoTrading Let’s set the working directory YOURPATH…

  • COVID-19 Data Visualization, Impact and Vaccine Sentiment Analysis

    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…

  • 50 Coronavirus COVID-19 Free APIs

    50 Coronavirus COVID-19 Free APIs

    Featured Photo by Edward Jenner on Pexels. As the Coronavirus pandemic, also known as COVID-19, continues to spread, staying up-to-date on the most recent information is essential. Keeping up with relevant information, such as new cases, deaths and local infection rates, can help you stay safe and healthy. Additionally, many of these APIs are free…

  • 99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow 2.11.0

    99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow 2.11.0

    Workflow The entire workflow is as follows: Prerequisites We need to install the following libraries: !pip install –user tensorflow BC Dataset In this study, we use the BC Wisconsin (Diagnostic) Dataset to predict whether the BC is benign or malignant. Model features are computed from a digitized image of a fine needle aspirate (FNA) of…

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

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

    Contents: BC Dataset Conventionally, the Breast Cancer Wisconsin (Diagnostic) Data Set has been used to predict whether the breast cancer is benign or malignant. Features were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. The dataset can…

  • A Roadmap from Data Science to BI via ML

    A Roadmap from Data Science to BI via ML

    This post describes a Data Science (DS) roadmap, with relevant business applications. It has been written for aspiring data scientists, technical experts who work with data scientists, data-driven technology stakeholders, or anyone interested in learning about what DS is and what it’s used for. Why DS: The average base salary of a data scientist in…

  • A Comparison of Binary Classifiers for Enhanced ML/AI Breast Cancer Diagnostics – 1. Scikit-Plot

    A Comparison of Binary Classifiers for Enhanced ML/AI Breast Cancer Diagnostics – 1. Scikit-Plot

    The goal of this post is a comparison of available binary classifiers in Scikit-Learn on the breast cancer (BC) dataset. The BC dataset comes with the Scikit-Learn package itself. Contents: Data Analysis Let’s set the working directory YOURPATH import osos.chdir(‘YOURPATH’) os. getcwd() and load the BC dataset from sklearn import datasetsdata = datasets.load_breast_cancer() with the…

  • ML/AI Breast Cancer Diagnosis with 98% Confidence

    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. 

  • Breast Cancer ML Classification – Logistic Regression vs Gradient Boosting with Hyperparameter Optimization (HPO)

    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…

  • A Comparative Analysis of Breast Cancer ML/AI Binary Classifications

    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…

  • An AWS Comparison of ML/AI Diabetes-2 Classification Algorithms

    An AWS Comparison of ML/AI Diabetes-2 Classification Algorithms

    Featured Photo by Myriam Zilles on Unsplash In the last decade, the impact of Type-2 Diabetes (T2D) has increased to a great extent especially in developing countries. Therefore, early diagnosis and classification of T2D has become an active area of healthtech. Numerous data-driven techniques are available to control T2D. This work presents a comparative use-case study of several…

  • HPO-Tuned ML Diabetes-2 Prediction

    HPO-Tuned ML Diabetes-2 Prediction

    This blog was inspired by the recent tests with Machine Learning (ML) hyper-parameter optimization (HPO) and cross-validation scores for enhanced prediction of Type-2 diabetes (T2D). The basic workflow includes RandomizedSearchCV HPO and accuracy metrics: The advanced workflow includes EDA, FE, HPO, and complete X-validation: Both workflows compare several ML algorithms, SMOTE oversampling, scaling and various…

  • HealthTech ML/AI Q3 ’22 Round-Up

    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…

  • AI-Guided Drug Recommendation

    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,…