The document provides an overview of different machine learning algorithms used to predict house sale prices in King County, Washington using a dataset of over 21,000 house sales. Linear regression, neural networks, random forest, support vector machines, and Gaussian mixture models were applied. Neural networks with 100 hidden neurons performed best with an R-squared of 0.9142 and RMSE of 0.0015. Random forest had an R-squared of 0.825. Support vector machines achieved 73% accuracy. Gaussian mixture modeling clustered homes into three groups and achieved 49% accuracy.
This document proposes a method for classifying breast cancer cells using unsupervised linear transformation (PCA) along with cosine similarity. It involves the following steps: (1) applying PCA to select robust features from a breast cancer dataset, (2) projecting the data into a lower dimensional space using the selected features, and (3) classifying the cells as normal, benign, or malignant using cosine similarity in the reduced dimensional space. Experiments show the accuracy increases from 78.9% without PCA to 99.12% when using the proposed PCA-Cosine similarity method, demonstrating its effectiveness for breast cancer classification.
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Diagnosis of lung cancer predictionsystem using data mining Classification T...Toushik Paul
This presentation discusses developing a lung cancer prediction system using data mining classification techniques. It begins with an introduction to lung cancer types, symptoms, and risk factors. It then discusses using knowledge discovery and data mining to extract patterns from medical data. Several data mining classification methods are examined, including decision trees, neural networks, and Bayesian networks. The presentation concludes that a naive Bayes model most effectively predicts lung cancer, but decision trees are easier to interpret and provide patient profile details.
- Pakistan has the highest incidence of breast cancer in Asia, with over 83,000 new cases reported annually. Breast cancer is the leading cause of cancer mortality among females in Pakistan.
- Early detection of breast cancer significantly improves survival rates, with a 100% 5-year survival rate for cancers detected early. However, Pakistan currently lacks a system for widespread breast cancer screening.
- Artificial intelligence can help by assisting oncologists in diagnosing breast cancer. A machine learning model trained on breast cancer data achieved over 96% accuracy in predicting malignant tumors, which could help detect cancers earlier.
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
This document summarizes a master's thesis that applied machine learning models to short-term electric load forecasting in Greece. It describes testing various machine learning algorithms like SVM, random forests, XGBoost and neural networks on electricity demand data from the Greek grid combined with meteorological data. The best performing models achieved a prediction error of 2.41%, outperforming the existing operator models. A shiny web application called ipto-ml was developed to allow for short-term load forecasting on new dates using the models.
The document provides an overview of different machine learning algorithms used to predict house sale prices in King County, Washington using a dataset of over 21,000 house sales. Linear regression, neural networks, random forest, support vector machines, and Gaussian mixture models were applied. Neural networks with 100 hidden neurons performed best with an R-squared of 0.9142 and RMSE of 0.0015. Random forest had an R-squared of 0.825. Support vector machines achieved 73% accuracy. Gaussian mixture modeling clustered homes into three groups and achieved 49% accuracy.
This document proposes a method for classifying breast cancer cells using unsupervised linear transformation (PCA) along with cosine similarity. It involves the following steps: (1) applying PCA to select robust features from a breast cancer dataset, (2) projecting the data into a lower dimensional space using the selected features, and (3) classifying the cells as normal, benign, or malignant using cosine similarity in the reduced dimensional space. Experiments show the accuracy increases from 78.9% without PCA to 99.12% when using the proposed PCA-Cosine similarity method, demonstrating its effectiveness for breast cancer classification.
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Diagnosis of lung cancer predictionsystem using data mining Classification T...Toushik Paul
This presentation discusses developing a lung cancer prediction system using data mining classification techniques. It begins with an introduction to lung cancer types, symptoms, and risk factors. It then discusses using knowledge discovery and data mining to extract patterns from medical data. Several data mining classification methods are examined, including decision trees, neural networks, and Bayesian networks. The presentation concludes that a naive Bayes model most effectively predicts lung cancer, but decision trees are easier to interpret and provide patient profile details.
- Pakistan has the highest incidence of breast cancer in Asia, with over 83,000 new cases reported annually. Breast cancer is the leading cause of cancer mortality among females in Pakistan.
- Early detection of breast cancer significantly improves survival rates, with a 100% 5-year survival rate for cancers detected early. However, Pakistan currently lacks a system for widespread breast cancer screening.
- Artificial intelligence can help by assisting oncologists in diagnosing breast cancer. A machine learning model trained on breast cancer data achieved over 96% accuracy in predicting malignant tumors, which could help detect cancers earlier.
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
This document summarizes a master's thesis that applied machine learning models to short-term electric load forecasting in Greece. It describes testing various machine learning algorithms like SVM, random forests, XGBoost and neural networks on electricity demand data from the Greek grid combined with meteorological data. The best performing models achieved a prediction error of 2.41%, outperforming the existing operator models. A shiny web application called ipto-ml was developed to allow for short-term load forecasting on new dates using the models.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Cancer is a disease caused by abnormal cell growth and can affect different cell types. This paper focuses on breast cancer, which is classified based on the type of affected cell. There are several risk factors that can increase the likelihood of developing cancer, including gender, age, genetics, family history, weight, alcohol use, and smoking. The dataset used contains information from 569 breast cancer cases, including demographic and cell feature data. Machine learning algorithms like decision trees can be applied to build models for diagnosing breast cancer based on these attributes with up to 96.46% accuracy.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
1. Perform architecture search over deep representations
2. Analyse the importance of hyperparameters.
House price ppt 18 bcs6588_md. tauhid alamArmanMalik66
This document discusses predicting housing prices using machine learning. It introduces the problem of helping buyers determine if a house price is fair. It then discusses using machine learning models trained on housing data to accurately predict prices. The document outlines the tools, libraries, data processing steps, and machine learning methods used to build a model that considers house features to predict sale prices.
At the 35th AICC-RCOG Annual Conference in association with FOGSI and MOGS, Dr. Niranjan Chavan, President of MOGS, gave an address on Artificial Intelligence in Gynaecologic Oncology at Taj Lands' End, Bandra, Mumbai on the 6th November 2022
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
This document discusses feature selection in machine learning and data mining. It begins by asking how to select the most important features from a set of features to reduce dimensionality while retaining discriminatory information. The document emphasizes the importance of preprocessing data before feature selection, including removing outliers, normalizing data to account for different feature scales, and handling missing data. It then discusses various statistical and mathematical techniques for feature selection such as hypothesis testing, scatter matrices, and sequential backward selection.
Radiomics and Deep Learning for Lung Cancer ScreeningWookjin Choi
The document summarizes research on using radiomics and deep learning approaches for lung cancer screening. It describes:
1) Using radiomic features like shape, texture, and intensity from lung nodules on CT scans and an SVM-LASSO model to classify nodules with 87.9% sensitivity and 78.2% specificity, outperforming the Lung-RADS system.
2) A deep learning model developed for a Kaggle competition that achieved 67.4% accuracy on nodule classification but only ranked 99th due to overfitting issues without enough data.
3) Future work could integrate quantification of nodule characteristics like spiculation with plasma biomarkers to improve diagnostic accuracy.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
The Titanic - machine learning from disasterMostafa Nizam
• Historical context to understand "What does the data mean?"
• Learn one data set well, and then apply different algorithms and modelling tools.
• This is a true event and everybody knows about the Titanic.
• Whole information is in the internet and the data is verified.
IRJET- Intelligent Prediction of Lung Cancer Via MRI Images using Morphologic...IRJET Journal
The document describes a proposed system to intelligently predict lung cancer using MRI images and morphological neural network analysis. The proposed system uses a three-stage approach: preprocessing MRI images, extracting features using wavelet decomposition and normalization, and classifying tissues as normal or abnormal using a morphological neural network with image pruning. This combination of morphological image processing and neural networks is intended to more efficiently classify cancer cells and identify affected regions than previous methods.
Health economic modelling in the diagnostics development processcheweb1
This document discusses the use of health economic modelling in the diagnostics development process. It highlights the need for early decision modelling to efficiently design clinical research studies for new diagnostics. Decision modelling can also be used to assess the potential clinical impact and cost-effectiveness of diagnostics across different stages of the validation process. The document describes an example of decision modelling used to help design the OPTIMA trial, which evaluated multiple biomarker tests for stratifying breast cancer treatment. Close collaboration between different stakeholders is important for effective diagnostics evaluation.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Cancer is a disease caused by abnormal cell growth and can affect different cell types. This paper focuses on breast cancer, which is classified based on the type of affected cell. There are several risk factors that can increase the likelihood of developing cancer, including gender, age, genetics, family history, weight, alcohol use, and smoking. The dataset used contains information from 569 breast cancer cases, including demographic and cell feature data. Machine learning algorithms like decision trees can be applied to build models for diagnosing breast cancer based on these attributes with up to 96.46% accuracy.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
1. Perform architecture search over deep representations
2. Analyse the importance of hyperparameters.
House price ppt 18 bcs6588_md. tauhid alamArmanMalik66
This document discusses predicting housing prices using machine learning. It introduces the problem of helping buyers determine if a house price is fair. It then discusses using machine learning models trained on housing data to accurately predict prices. The document outlines the tools, libraries, data processing steps, and machine learning methods used to build a model that considers house features to predict sale prices.
At the 35th AICC-RCOG Annual Conference in association with FOGSI and MOGS, Dr. Niranjan Chavan, President of MOGS, gave an address on Artificial Intelligence in Gynaecologic Oncology at Taj Lands' End, Bandra, Mumbai on the 6th November 2022
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
This document discusses feature selection in machine learning and data mining. It begins by asking how to select the most important features from a set of features to reduce dimensionality while retaining discriminatory information. The document emphasizes the importance of preprocessing data before feature selection, including removing outliers, normalizing data to account for different feature scales, and handling missing data. It then discusses various statistical and mathematical techniques for feature selection such as hypothesis testing, scatter matrices, and sequential backward selection.
Radiomics and Deep Learning for Lung Cancer ScreeningWookjin Choi
The document summarizes research on using radiomics and deep learning approaches for lung cancer screening. It describes:
1) Using radiomic features like shape, texture, and intensity from lung nodules on CT scans and an SVM-LASSO model to classify nodules with 87.9% sensitivity and 78.2% specificity, outperforming the Lung-RADS system.
2) A deep learning model developed for a Kaggle competition that achieved 67.4% accuracy on nodule classification but only ranked 99th due to overfitting issues without enough data.
3) Future work could integrate quantification of nodule characteristics like spiculation with plasma biomarkers to improve diagnostic accuracy.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
The Titanic - machine learning from disasterMostafa Nizam
• Historical context to understand "What does the data mean?"
• Learn one data set well, and then apply different algorithms and modelling tools.
• This is a true event and everybody knows about the Titanic.
• Whole information is in the internet and the data is verified.
IRJET- Intelligent Prediction of Lung Cancer Via MRI Images using Morphologic...IRJET Journal
The document describes a proposed system to intelligently predict lung cancer using MRI images and morphological neural network analysis. The proposed system uses a three-stage approach: preprocessing MRI images, extracting features using wavelet decomposition and normalization, and classifying tissues as normal or abnormal using a morphological neural network with image pruning. This combination of morphological image processing and neural networks is intended to more efficiently classify cancer cells and identify affected regions than previous methods.
Health economic modelling in the diagnostics development processcheweb1
This document discusses the use of health economic modelling in the diagnostics development process. It highlights the need for early decision modelling to efficiently design clinical research studies for new diagnostics. Decision modelling can also be used to assess the potential clinical impact and cost-effectiveness of diagnostics across different stages of the validation process. The document describes an example of decision modelling used to help design the OPTIMA trial, which evaluated multiple biomarker tests for stratifying breast cancer treatment. Close collaboration between different stakeholders is important for effective diagnostics evaluation.
Technology Assessment, Outcomes Research and Economic Analysesevadew1
This document discusses technology assessment, outcomes research, and economic analyses in healthcare. It provides background on rising healthcare costs in the US and outlines a hierarchy for assessing new medical technologies from technical efficacy to patient and societal outcomes. Randomized controlled trials are described as the gold standard but limitations are noted. Alternative study designs like modeling and assessing intermediate outcomes are proposed when RCTs are not feasible. The document uses CT for appendicitis as an example to work through initial steps in outcomes research. It also discusses limitations and alternative outcomes like assessing the therapeutic value of diagnostic tests.
Low Dose CT Screening for Early Diagnosis of Lung CancerKue Lee
This document summarizes the evidence and guidelines for low-dose CT screening for lung cancer. It discusses the National Lung Screening Trial which found a 20% reduction in lung cancer mortality with low-dose CT screening in high-risk individuals. However, screening also led to many false positives in 96.4% of cases. Guidelines from the USPSTF recommend annual screening for ages 55-80 who have at least a 30 pack-year smoking history if they currently smoke or quit within the past 15 years. Primary care providers have an important role in facilitating shared decision making about the benefits and harms of screening.
This document discusses technology assessment, outcomes research, and economic analyses in healthcare. It provides background on rising healthcare costs in the US without clear improvements in health outcomes compared to other countries. The rationale for assessing new technologies and their impact is described. Key aspects of technology assessment are outlined, including technical efficacy, diagnostic accuracy, diagnostic impact, therapeutic impact, patient outcomes, and societal outcomes. Challenges with randomized controlled trials in assessing technologies are reviewed. The National Lung Screening Trial is presented as an example. Finally, computed tomography for appendicitis is analyzed as a hypothetical example of how modeling could be used to assess a technology when a randomized trial may not be feasible.
YOLOv8-Based Lung Nodule Detection: A Novel Hybrid Deep Learning Model ProposalIRJET Journal
This document proposes a novel deep learning model for automated real-time detection of lung nodules using chest CT scans. A two-stage model is proposed that first uses a CNN to detect nodule regions with 94% accuracy, then fine-tunes a YOLOv8 object detection model on the detected regions. When tested on the LUNA16 dataset, the YOLOv8m configuration achieved 92.3% accuracy, 88.5% sensitivity, and 53.5% mean average precision for nodule detection, outperforming existing methods. The proposed hybrid model shows potential for improving nodule detection accuracy and efficiency for early lung cancer screening.
- National challenges in cancer research include lowering barriers to data access and analysis, and integrating clinical and basic research data to enable improved outcomes.
- Disruptive technologies like high-throughput biology and ubiquitous computing are generating large amounts of molecular and clinical cancer data.
- The NCI is working to build infrastructure like the Genomics Data Commons and Cloud Pilots to make these data widely accessible and support data analysis.
- The goal is to develop a national "learning health system" that applies insights from real-world cancer data to research and clinical practice to continuously improve patient care and outcomes.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
IRJET- Survey Paper on Oral Cancer Detection using Machine LearningIRJET Journal
This document discusses several papers on using machine learning techniques for oral cancer detection. It first provides background on oral cancer and the importance of early detection. It then summarizes five research papers that used different machine learning and data mining approaches for oral cancer classification and detection, including using algorithms like Naive Bayes, J48, and SVM on clinical datasets, as well as analyzing oral microbiome data using metagenomics and machine learning models. The goal is to evaluate machine learning as a domain for early oral cancer detection by analyzing patient datasets and developing predictive and classification rules.
The guideline recommends annual low-dose CT screening for lung cancer until age 79 for high-risk groups based on results from the National Lung Screening Trial. It received moderate ratings in the AGREE assessment due to lack of stakeholder involvement, uncertainty around harms and costs, and need for further validation. While screening high-risk groups could reduce mortality, the high false positive rate and risk of overdiagnosis require careful consideration in implementation.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
This document discusses various data mining techniques for cancer diagnosis and prognosis. It begins by explaining how data mining utilizes databases, statistics, machine learning and pattern recognition. It then focuses on techniques for breast cancer analysis, including decision trees to identify vulnerability, neural networks and association rules for mammography classification, naive Bayes for survival rates, logistic regression and support vector machines to identify treatment effectiveness. Finally, it discusses using Bayesian networks to differentiate poor and good prognosis and proposes future work developing web apps based on these techniques.
The document discusses key aspects of randomized controlled trials (RCTs) including study designs, characteristics, and methods for critical appraisal. RCTs are considered the gold standard for evaluating interventions as randomization reduces bias. Key characteristics discussed include eligibility criteria, interventions, outcomes, sample size calculation, allocation concealment, blinding, and intention-to-treat analysis. Statistical methods to analyze RCT data and compare groups are also presented, such as relative risk, odds ratios, and number needed to treat. Tools for critical appraisal include considering the precision of treatment effect sizes, applicability of results, and weighing benefits versus harms.
Oncotype DX is a prognostic test that uses gene expression profiles in tumors to predict the risk of breast cancer recurrence. It has been recommended in the UK to help guide chemotherapy decisions for women with early-stage, hormone receptor-positive breast cancer who are assessed to have intermediate risk. The test results categorize a patient's risk as low (score <18), intermediate (score 18-30), or high (score >30) to help determine if the benefits of chemotherapy outweigh the risks. Adjuvant! Online is a similar web-based tool that uses factors like age, tumor size, and hormone receptor status to calculate survival probabilities and expected benefit of adjuvant therapy. Treatment decisions for early-stage breast cancer consider
Next generation sequencing (NGS) of circulating tumor DNA (ctDNA) from patient plasma is becoming more widespread in oncology clinical trials. The noninvasive nature of acquiring these samples is particularly important when resection of representative tumor samples is not advised or not possible. However, profiling of ctDNA has challenges to overcome, such as low concentration of ctDNA shed from the tumor and a low signal:noise ratio caused by somatic alterations with less than 1% variant allele fraction. Improving the sensitivity of these assays to detect low allele frequency events with high confidence requires robust sequencing of low input libraries while employing error correction to reduce background noise. To overcome these challenges, we have incorporated unique molecular identifiers (UMIs) into our NGS workflow. Using these novel adapters paired with our proprietary bioinformatics pipeline (AstraZeneca), the number of false positive variants reported for allele fractions less than 0.5% was reduced tenfold. We also refined our curation based on the mapping quality and strand bias in the vicinity of each variant to further reduce the background noise. The use of xGen® Dual Index UMI Adapters—Tech Access (Integrated DNA Technologies) has enabled us to sequence thousands of plasma samples from diverse tumor indications and at differing time points during our trials. The generated data are highly informative with the potential to answer critical questions relating to individual response or resistance to experimental therapies. During this webinar, we discuss our current NGS ctDNA workflow and our future plans to increase our sequencing sensitivity with these novel UMI adapters.
Quality measurement in cardiac surgery aims to improve outcomes by systematically tracking morbidity and mortality rates. Initially, unadjusted outcomes did not account for patient risk factors. This led to the development of risk-adjustment models like the Aristotle score and RACHS-1 score to stratify complexity and risk. The STS National Database was also created to provide standardized, risk-adjusted data from a large benchmark population. Effective quality measurement considers risk factors, standardized data, and outcomes beyond just mortality rates. Ongoing enhancements continue to advance cardiac surgery quality.
Design of an Intelligent System for Improving Classification of Cancer DiseasesMohamed Loey
The methodologies that depend on gene expression profile have been able to detect cancer since its inception. The previous works have spent great efforts to reach the best results. Some researchers have achieved excellent results in the classification process of cancer based on the gene expression profile using different gene selection approaches and different classifiers
Early detection of cancer increases the probability of recovery. This thesis presents an intelligent decision support system (IDSS) for early diagnosis of cancer-based on the microarray of gene expression profiles. The problem of this dataset is the little number of examples (not exceed hundreds) comparing to a large number of genes (in thousands). So, it became necessary to find out a method for reducing the features (genes) that are not relevant to the investigated disease to avoid overfitting. The proposed methodology used information gain (IG) for selecting the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the Gray Wolf Optimization algorithm (GWO). Finally, the methodology exercises support vector machine (SVM) for cancer type classification. The proposed methodology was applied to three data sets (breast, colon, and CNS) and was evaluated by the classification accuracy performance measurement, which is most important in the diagnosis of diseases. The best results were gotten when integrating IG with GWO and SVM rating accuracy improved to 96.67% and the number of features was reduced to 32 feature of the CNS dataset.
This thesis investigates several classification algorithms and their suitability to the biological domain. For applications that suffer from high dimensionality, different feature selection methods are considered for illustration and analysis. Moreover, an effective system is proposed. In addition, Experiments were conducted on three benchmark gene expression datasets. The proposed system is assessed and compared with related work performance.
Frederique Penault Llorca : Prosigna : un test décentralisé apporte t il une ...breastcancerupdatecongress
The document discusses several gene expression assays for breast cancer, including Oncotype DX, MammaPrint, and Prosigna. It provides details on:
- Oncotype DX and MammaPrint are centralized tests that analyze gene expression from tumor samples sent to a central lab.
- Prosigna uses the PAM50 gene signature and can be performed locally using the Nanostring nCounter system. It analyzes gene expression from formalin-fixed paraffin-embedded samples.
- The document discusses analytical validation of the assays and provides specifics on the genes and biomarkers analyzed by each test. It also describes how the results from these assays can guide treatment decisions.
H2O World - H2O for Genomics with Hussam Al-Deen AshabSri Ambati
GenomeDx Biosciences is a clinical genomics company that uses machine learning on genomic data to develop clinical tests for cancer. They developed a genomic classifier to predict prostate tumor Gleason grade using RNA expression data from over 7,000 patients. This Gleason grade classifier was tested on a separate dataset and achieved an AUC of 0.77, outperforming other clinical predictors. The classifier also predicted metastatic outcomes with an AUC of 0.73, demonstrating its ability to predict patient risk. GenomeDx uses the H2O platform for its machine learning work due to its ability to handle high-dimensional genomic data and its deep learning algorithms, which can model complex nonlinear relationships between genes.
Data Science in Healthcare -The University Malaya Medical Centre Breast Cance...University of Malaya
The document discusses an MRI report for a patient with a history of metastatic breast cancer. The MRI showed abnormal high signal intensity lesions in multiple vertebral bodies, sacrum, ilium and sternum, consistent with known metastatic disease. Correlation was made with a previous CT from two months prior.
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Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
2. INTRODUCTION
• Lung Cancer is the number one cause of all cancer deaths in the US, estimated
234,030 new cases and 154,050 deaths in 2018.
• Early detection using low-dose computed tomography (CT) Screening on high risk
individuals can reduce lung cancer mortality by 20%.
• The current CT screening criteria are 55-77 years old adults, currently smoking, and
30 pack-year smoking history, but these simple criteria are relatively ineffective.
• Many researches suggest that using lung cancer risk prediction models could lead
to more effective screening programs compared to the current screening criteria.
3. • Develop two risk prediction models for Lung Cancer using classification
algorithms in R.
Decision Tree – Classification and Regression Tree ( CART)
Neural Network – Artificial Neural Network (ANN)
• Select the better model base on their performance metrics.
• Identify the major risk factors associated with lung cancer.
PROJECT PURPOSE
4. Variables Characteristic
Patient ID Character
Age Numeric 14-73
Gender Binary 1-2
Smoking Numeric 1-8
Passive Smoking Numeric 1-8
Air Pollution Numeric 1-8
Occupational Hazards Numeric 1-8
Genetic Risk Numeric 1-7
Alcohol Use Numeric 1-7
Chronic Lung Disease Numeric 1-7
Dust Allergy Numeric 1-7
Diet Balance Numeric 1-7
Chest Pain Numeric 1-9
Short Breath Numeric 1-9
Fatigue Numeric 1-9
Bloody Coughing Numeric 1-9
Wheezing Numeric 1-7
Swallowing Difficulty Numeric 1-7
Clubbing of finger nails Numeric 1-7
Weight Loss Numeric 1-7
Frequent Cold Numeric 1-7
Dry Cough Numeric 1-7
Clubbing of finger nails Numeric 1-9
Levels Chr /Binary High, Medium, Low
DATA
DESCRIPTION
• Data is a subset of the National Lung
Screening Trial Cohort
• 1000 randomized participants
• 22 attributes are potential risk
factors and symptoms of lung
cancer
• Each observation has one of 3
possible classes: Low, Medium, High
10. MODEL EVALUATION
Models Accuracy Sensitivity Specificity Precision ROC Area
Decision Tree
(High Level)
.9832 .9541 1 1 .9721
Neural Network
(High Level)
.9899 1 .9841 .9732 .9636
11. DISCUSSION
• In medical test, False Negative is more dangerous than False Positive, so Finale risk prediction model is
Artificial Neural Network model which has 100% Sensitivity (0% False Negative) compared to Decision
Tree 95.41% Sensitivity (4.59% False Negative).
• Based on Variable Importance result, the most significant risk factors for lung cancer are Air Pollution,
Age, Smoking, Passive Smoking, and Alcohol Use.
• Future improvements
Improve the model performance by fine-tuning the model parameters
Reduce input features to prevent overfitting.
Increase data inputs for better model performance.
Use different classification algorithms for better selection ( Support Vector Machine, RandomForest)
12. • The project has developed the risk prediction model for Lung Cancer and identified top
5 risk factors associated with Lung cancer using classification methods in R packages.
• Using risk prediction models to select high-risk individuals for lung cancer screening
would be more superior to current selection criteria.
• Avoiding the major risk factors may help to prevent and lower lung cancer.
• The project shows that the results are promising for the application of lung cancer risk
prediction models for selective screening.
CONCLUSION
13. • American Lung Association http://www.lung.org
• National Lung Screening Trials https://www.cancer.gov/types/lung/research/nlst
• Fitting a neural network in R https://www.r-bloggers.com
• Classification And Regression Trees for Machine Learning https://machinelearningmastery.com
• Machine Learning in Medicine, Rahul C. Deo, Circulation. 2015;132:1920-1930, November 16,
2015
• Evaluation of Classification Model Accuracy: Essentials http://www.sthda.com/english/articles
• Cross-Validation for Predictive Analytics using R http://www.milanor.net/blog/cross-validation-
for-predictive-analytics-using-r/
• Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati,March 15, 2017
• R packages https://cran.r-project.org/web/packages
REFERENCES