Tag: healthtech

  • 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 from the University of Wisconsin Hospitals, Madison, Wisconsin, USA. Feature engineering yields 9 dominant features Results: AUC(LR)=0.987 > AUC(GBM)=0.975 It is clear that LR has the highest f1-score and POS.

  • 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 objective is to compare different supervised ML, deep learning (DL) and data mining techniques for the early detection of BC. The idea is to analyze BC data based on its characteristics and identify the effectiveness of clustering and classification instructions for analyzing and fitting various ML models. We tested the performance of ML models by looking at their accuracies, sensitivities, specificities, and other metrics. Results obtained with the best ML model with most dominant features included showed the highest classification accuracy (~99%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the BC research.

  • 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, among others.