Category: Breast Cancer
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MLflow SHAP & Transformers
The post covers simplified MLflow projects for reproducible and reusable data science code. It details local environment setup, ElasticNet model optimization, and SHAP explanations for breast cancer, diabetes, and iris datasets. Additionally, it showcases MLflow Sentence Transformers for a chatbot and translation. This demonstrates their powerful interface for managing transformer models from libraries like Hugging…
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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 Scikit Learn Algorithms for Breast Cancer Classification – 2. Cross Validation vs Performance
The post is a continuation of a previous breast cancer study comparing Scikit-Learn binary classifiers for cross validation and model performance. The classifiers compared include Logistic Regression, GaussianNB, SVC, KNN, Random Forest, Extra Trees, and Gradient Boosting. Learning curves show the comparison of classifier performance. Results indicate GaussianNB is more efficient than SVC in terms…
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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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Cloud-Native Tech Autumn 2022 Fair
Let’s dive deeper into the cloud-native tech trends and features to follow in Q4 2022 and beyond. Contents: Markets Services Serverless Cybersecurity DevSecOps ML/AI/IoT Use-Cases Events Training Explore More Infographic
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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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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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Supervised ML/AI Breast Cancer Diagnostics (BCD) – The Power of HealthTech
Pilots Related to HealthTech Infographic These plots illustrate the most basic application of ML/AI in BCD as the binary classification problem. Classification usually refers to any kind of problem where a specific type of class label is the result to be predicted from the given input field of data. This is a task which assigns a label value…
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