PREDICTION OF BREAST CANCER USING DATA MINING TECHNIQUESIAEME Publication
Women who have improved from breast cancer (BC) constantly panic about setback. The way that they have persevered through the meticulous treatment makes repeat their biggest fear. However, with current spreads in technology, early repeat prediction can enable patients to get treatment prior. The accessibility of broad information and propelled techniques make precise and fast prediction possible. This examination expects to think about the exactness of a couple of existing information mining calculations in predicting BC repeat. It inserts a particle swarm optimization as highlight choice into ANN classifier. An objective of increasing the accuracy level of the prediction model.
Comparative analysis on bayesian classification for breast cancer problemjournalBEEI
The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
PREDICTION OF BREAST CANCER USING DATA MINING TECHNIQUESIAEME Publication
Women who have improved from breast cancer (BC) constantly panic about setback. The way that they have persevered through the meticulous treatment makes repeat their biggest fear. However, with current spreads in technology, early repeat prediction can enable patients to get treatment prior. The accessibility of broad information and propelled techniques make precise and fast prediction possible. This examination expects to think about the exactness of a couple of existing information mining calculations in predicting BC repeat. It inserts a particle swarm optimization as highlight choice into ANN classifier. An objective of increasing the accuracy level of the prediction model.
Comparative analysis on bayesian classification for breast cancer problemjournalBEEI
The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
Breast Cancer Diagnostics with Bayesian NetworksBayesia USA
The Wisconsin Breast Cancer Database (WBCD) is a widely studied (and publicly available) data set from the field of breast cancer diagnostics. The creators of this database, Wolberg, Street, Heisey and Managasarian, made an important contribution with their research towards automating diagnostics with image processing and machine learning.
Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump
of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among
women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can
affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less
than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early
detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available
dataset containing features of breast tumors was utilized to identify breast tumors using machine learning
and deep learning techniques. Various prediction models were constructed, including logistic regression
(LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB),
Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These
models were trained to classify and predict breast tumor cases based on the provided features.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Predictive modeling for breast cancer based on machine learning algorithms an...IJECEIAES
Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
Breast Cancer Diagnostics with Bayesian NetworksBayesia USA
The Wisconsin Breast Cancer Database (WBCD) is a widely studied (and publicly available) data set from the field of breast cancer diagnostics. The creators of this database, Wolberg, Street, Heisey and Managasarian, made an important contribution with their research towards automating diagnostics with image processing and machine learning.
Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump
of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among
women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can
affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less
than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early
detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available
dataset containing features of breast tumors was utilized to identify breast tumors using machine learning
and deep learning techniques. Various prediction models were constructed, including logistic regression
(LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB),
Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These
models were trained to classify and predict breast tumor cases based on the provided features.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Predictive modeling for breast cancer based on machine learning algorithms an...IJECEIAES
Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
1. BREAST CANCER USING
MACHINE LEARNING
Submitted by
S.Rajayogha
2nd M.sc Data science
Kalasalingam Academy of Research and education
9921146010@klu.ac.in
2. OVERVIEW
• Introduction
• Breast cancer- an overview
• Why I am using machine learning?
• Understanding the algorithm
• Analysis with R language
• Confusion matrix and accuracy
3. INTRODUCTION
what is machine learning and why we are using it?
Machine learning(ML) is a field of artificial intelligence that uses statistical techniques to
give computer systems the ability to “learn” from data ,without being explicity programmed.
4. BREAST CANCER: AN OVERVIEW
• Breast cancer is the second leading cause of cancer death in women , second only
to lung cancer.
• The Leading risk factor for breast cancer is simply being a woman . though breast
cancer does occur in men , the disease is 100 times more common in women.
• Men can also get breast cancer .In 2017,the American cancer society estimates 2,470
new cases of invasive breast cancer will be diagnosed in men in the US.
• A Women has about a one in eight chance of being diagnosed with breast cancer in
her lifetime , according to the national cancer institute
• Most women who get breast cancer do not have a family history of the diseases.
• But women who have close blood relatives with breast cancer have a higher risk
.Having a first –degree relative(mother , sister or daughter) with breast cancer
almost doubles a woman’s risk.
6. UNDERSTANDING THE ALGORITHM
Lazy learning-Classification using Nearest Neighbors
K-Nearest Neighbor classifiers are defined by their characteristic of classifying
unlabeled examples by assigning them the class of similar labelled.
Examples:
Computer vision applications,including optical character recogonization and facial
recogonization in both still images and video.
Predicting whether a person will enjoy a movie or music recommendation.
Identifying patterns in generic data,perhaps to use them in detectingspecific
proteins or diseases.
9. ANALYSIS WITH R LANGUAGE
STEP 1 –COLLECTING DATA
We will utilize the Wisconsin Breast Cancer Diagnostic dataset from the UCI
Machine Learning Repository at http://archieve.ics.uci.edu/ml. This data was donated
by researcher of the university of winsconsin . The breast cancer data includes 569
example of cancer biopesies,each with 32 features
10. Step 2-exploring and preparing the data
Importing the data set in R IDE
>wbcd <- read.csv(“wisc_bc_data.csv”, stringAsFactors =FALSE)
The Table() indicates that 357 masses are benign while 212 are malignant
>table(wbcd$diagnosis)
B M
357 212
11. Step 3-training a model on the data
> wbcd_test_pred <- knn(train = wbcd_train, test = wbcd_test, cl =
wbcd_train_labels_k =21)
12. CONFUSION MATRIX AND ACCURACY
CONFUSION MATRIX is a matrix used to determine the performance of the
classification models for a given set of test data .it can only be determined if the value
for test data are known.
confusion matrix to calculate Accuracy
13. CONFUSION MATRIX AND ACCURACY
Accuracy is a metric used in classification problems used tell the percentage of
accurate predictions.we calculate it by dividing the no of correct predictions by the
total number of predictions.