Science is objective, since Breast Cancer is related to Hormones (estrogens, known human carcinogens), we should consider hormones. That's one reason why men have less risk.
\nBesides, hormones therapy on NEXT analysis could be included, since it's used by transgender.
\n\"The dataset for this challenge contains radiographic breast images of female subjects. \"
\nhttps://www.kaggle.com/competitions/rsna-breast-cancer-detection/data
\"Studies have shown that a woman’s risk of breast cancer is related to the estrogen and progesterone made by her ovaries (known as endogenous estrogen and progesterone). Being exposed for a long time and/or to high levels of these hormones has been linked to an increased risk of breast cancer.\"
\n\n\" Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. \"
\nMachine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset
\nCitation: Li, J., & Mani, G. (2021). Machine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset. Journal of Student Research, 10(3). https://doi.org/10.47611/jsrhs.v10i3.2199
\n\" People who are unaware of the potential danger of getting breast cancer like males would not have the medical awareness beforehand for predictions. Therefore, the PLCO ( The Prostate, Lung, Colorectal, and Ovarian) trials dataset consisting of ages, prostate status, marriage status etc. from National Institute of Cancer is used in this research for detection.\"
\n\" The main purpose of using PLCO test is to discover the potential risk of getting an Male Breast Cancer (MBC) as soon as possible with low cost and easy collection. It is the rarity of MBC that imposes the threat for males who are unaware of the danger. To explore the relatively most suitable models to use for detecting MBC using non-traditional PLCO test dataset, different existing models including decision tree, random forest, DBSCAN, One Class SVM and so on were used to fit the data.\"
\n\" Due to its extremity of imbalance, evaluation comes from the combination of standard accuracy and Area Under the Receiver Operating Characteristics(AUROC) for the overall accuracy of those models mentioned above. K-means and Logistic Regression models performed best with the AUC score of 0.62 and 0.67. Results suggested that more efficient approaches for common male breast cancer diagnosis or more advanced models and algorithms are needed in further study.
\nhttps://www.jsr.org/hs/index.php/path/article/view/2199
\nBreast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches
\nAuthors: Mohammad Monirujjaman Khan, Tahia Tazin, Mohammad Zunaid Hussain, Monira Mostakim, Taeefur Rehman, Samender Singh, Vaishali Gupta, and Othman Alomeir.
\nVolume 2022 | Article ID 6333573 | https://doi.org/10.1155/2022/6333573
\nTransgender women are more likely than cisgender men to acquire breast cancer.
Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer.\"
\n\"A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.\"
https://www.hindawi.com/journals/cin/2022/6333573/
\n\"Breast cancer in men is very rare, but around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"
\n\"The Male Breast Cancer Study was established to pinpoint the precise genetic, environmental and lifestyle causes of breast cancer in men, which will enable us to identify those who are at risk and understand what can be done to lower the chances of developing the disease.\"
\n\"The study also aims to identify similarities and differences between breast cancer in men and women.\"
\n\nOn the next analysis, maybe we can embrace all genders. Studies have already done it, as I have described above.
\n\"Breast cancer in men is very rare: Yearly, 2,600 men in the United States are diagnosed with breast cancer. And, around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"
\n\"Transgender and nonbinary people have the same basic health care needs as cisgender people. Trans and nonbinary people always deserve to be treated respectfully when they get health care, whether it’s specific to gender or not.\"
\n", "messages": [ { "id": 2050593, "postDate": "2022-11-30T21:28:42.260Z", "content": "Science is objective, since Breast Cancer is related to Hormones (estrogens, known human carcinogens), we should consider hormones. That's one reason why men have less risk.
\nBesides, hormones therapy on NEXT analysis could be included, since it's used by transgender.
\n\"The dataset for this challenge contains radiographic breast images of female subjects. \"
\nhttps://www.kaggle.com/competitions/rsna-breast-cancer-detection/data
\"Studies have shown that a woman’s risk of breast cancer is related to the estrogen and progesterone made by her ovaries (known as endogenous estrogen and progesterone). Being exposed for a long time and/or to high levels of these hormones has been linked to an increased risk of breast cancer.\"
\n\n\" Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. \"
\nMachine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset
\nCitation: Li, J., & Mani, G. (2021). Machine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset. Journal of Student Research, 10(3). https://doi.org/10.47611/jsrhs.v10i3.2199
\n\" People who are unaware of the potential danger of getting breast cancer like males would not have the medical awareness beforehand for predictions. Therefore, the PLCO ( The Prostate, Lung, Colorectal, and Ovarian) trials dataset consisting of ages, prostate status, marriage status etc. from National Institute of Cancer is used in this research for detection.\"
\n\" The main purpose of using PLCO test is to discover the potential risk of getting an Male Breast Cancer (MBC) as soon as possible with low cost and easy collection. It is the rarity of MBC that imposes the threat for males who are unaware of the danger. To explore the relatively most suitable models to use for detecting MBC using non-traditional PLCO test dataset, different existing models including decision tree, random forest, DBSCAN, One Class SVM and so on were used to fit the data.\"
\n\" Due to its extremity of imbalance, evaluation comes from the combination of standard accuracy and Area Under the Receiver Operating Characteristics(AUROC) for the overall accuracy of those models mentioned above. K-means and Logistic Regression models performed best with the AUC score of 0.62 and 0.67. Results suggested that more efficient approaches for common male breast cancer diagnosis or more advanced models and algorithms are needed in further study.
\nhttps://www.jsr.org/hs/index.php/path/article/view/2199
\nBreast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches
\nAuthors: Mohammad Monirujjaman Khan, Tahia Tazin, Mohammad Zunaid Hussain, Monira Mostakim, Taeefur Rehman, Samender Singh, Vaishali Gupta, and Othman Alomeir.
\nVolume 2022 | Article ID 6333573 | https://doi.org/10.1155/2022/6333573
\nTransgender women are more likely than cisgender men to acquire breast cancer.
Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer.\"
\n\"A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.\"
https://www.hindawi.com/journals/cin/2022/6333573/
\n\"Breast cancer in men is very rare, but around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"
\n\"The Male Breast Cancer Study was established to pinpoint the precise genetic, environmental and lifestyle causes of breast cancer in men, which will enable us to identify those who are at risk and understand what can be done to lower the chances of developing the disease.\"
\n\"The study also aims to identify similarities and differences between breast cancer in men and women.\"
\n\nOn the next analysis, maybe we can embrace all genders. Studies have already done it, as I have described above.
\n\"Breast cancer in men is very rare: Yearly, 2,600 men in the United States are diagnosed with breast cancer. And, around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"
\n\"Transgender and nonbinary people have the same basic health care needs as cisgender people. Trans and nonbinary people always deserve to be treated respectfully when they get health care, whether it’s specific to gender or not.\"
\n", "rawMarkdown": "#Hormones influence on Breast Cancer\n\nScience is objective, since Breast Cancer is related to Hormones (estrogens, known human carcinogens), we should consider hormones. That's one reason why men have less risk. \n\nBesides, hormones therapy on NEXT analysis could be included, since it's used by transgender.\n\n#On this Competition images are only of female patients:\n\n \"The dataset for this challenge contains radiographic breast images of female subjects. \"\nhttps://www.kaggle.com/competitions/rsna-breast-cancer-detection/data\n\n#Estrogen and Progesterone Risks\n\n\"Studies have shown that a woman’s risk of breast cancer is related to the estrogen and progesterone made by her ovaries (known as endogenous estrogen and progesterone). Being exposed for a long time and/or to high levels of these hormones has been linked to an increased risk of breast cancer.\"\n\nhttps://www.cancer.gov/about-cancer/causes-prevention/risk/hormones#:~:text=Studies%20have%20also%20shown%20that,increased%20risk%20of%20breast%20cancer. \n\n#Male risk to breast cancer\n\n\" Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. \"\n\n#Machine Learning and Male Breast Cancer\n\nMachine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset\n\nCitation: Li, J., & Mani, G. (2021). Machine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset. Journal of Student Research, 10(3). https://doi.org/10.47611/jsrhs.v10i3.2199\n\n\" People who are unaware of the potential danger of getting breast cancer like males would not have the medical awareness beforehand for predictions. Therefore, the PLCO ( The Prostate, Lung, Colorectal, and Ovarian) trials dataset consisting of ages, prostate status, marriage status etc. from National Institute of Cancer is used in this research for detection.\"\n\n\" The main purpose of using PLCO test is to discover the potential risk of getting an Male Breast Cancer (MBC) as soon as possible with low cost and easy collection. It is the rarity of MBC that imposes the threat for males who are unaware of the danger. To explore the relatively most suitable models to use for detecting MBC using non-traditional PLCO test dataset, different existing models including decision tree, random forest, DBSCAN, One Class SVM and so on were used to fit the data.\"\n\n\n\" Due to its extremity of imbalance, evaluation comes from the combination of standard accuracy and Area Under the Receiver Operating Characteristics(AUROC) for the overall accuracy of those models mentioned above. K-means and Logistic Regression models performed best with the AUC score of 0.62 and 0.67. Results suggested that more efficient approaches for common male breast cancer diagnosis or more advanced models and algorithms are needed in further study.\n\nhttps://www.jsr.org/hs/index.php/path/article/view/2199\n\n#Breast Tumor Detection and CNN\n\nBreast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches\n\nAuthors: Mohammad Monirujjaman Khan, Tahia Tazin, Mohammad Zunaid Hussain, Monira Mostakim, Taeefur Rehman, Samender Singh, Vaishali Gupta, and Othman Alomeir.\n\nVolume 2022 | Article ID 6333573 | https://doi.org/10.1155/2022/6333573\n\n- Transgender women are more likely than cisgender men to acquire breast cancer.\n\n- Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer.\"\n\n \"A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.\"\n\nhttps://www.hindawi.com/journals/cin/2022/6333573/\n\n#What is the Male Breast Cancer Study?\n\n\"Breast cancer in men is very rare, but around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"\n\n\"The Male Breast Cancer Study was established to pinpoint the precise genetic, environmental and lifestyle causes of breast cancer in men, which will enable us to identify those who are at risk and understand what can be done to lower the chances of developing the disease.\"\n\n\"The study also aims to identify similarities and differences between breast cancer in men and women.\"\n\nhttps://breastcancernow.org/breast-cancer-research/research-projects/our-research-projects/male-breast-cancer-study\n\n#This topic isn't about gender identity. It's about health and Science.\n\nOn the next analysis, maybe we can embrace all genders. Studies have already done it, as I have described above.\n\n\"Breast cancer in men is very rare: Yearly, 2,600 men in the United States are diagnosed with breast cancer. And, around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"\n\n\"Transgender and nonbinary people have the same basic health care needs as cisgender people. Trans and nonbinary people always deserve to be treated respectfully when they get health care, whether it’s specific to gender or not.\"\n\nhttps://www.plannedparenthood.org/learn/gender-identity/transgender/what-do-i-need-know-about-trans-health-care", "votes": -3 }, { "id": 2065856, "postDate": "2022-12-15T06:15:59.513Z", "rawMarkdown": "", "votes": -1, "isDeleted": true, "replies": [ { "id": 2066178, "postDate": "2022-12-15T13:12:11.330Z", "content": "Hi Crase, weka511
\nI received many downvotes here (more than 6).
\nI've only transcribed research material, since researchers are already working with that subject.
\nhttps://www.hindawi.com/journals/cin/2022/6333573/
It's well-known that anyone that is submitted to hormones therapy can suffer consequences of the use of them. Just remember what happened to many elite athletes.
\nIn this case (Breast Cancer), estrogens, which could affect in the future those that are submmited to gender interventions.
\nMy topic is Not advocating for any cause. As I wrote at the beginning: Science is objective. Breast cancer is connected to hormons.
\nBreaking the 4th Wall. Listing reasons for been downvoted:
\nThey are focused on ML and forget to think critically about who is going to feed their Machine to be analysed. In diagnosis, if you don't provide all the features your diagnostic will be wrong. If people that provide the features have any prejudice, all the rest will lead to incorrect solutions/results.
\nThey don't read. Many read on mobiles. Reading only the titles or what is in the Newsfeed. Many just vote who are their equals and who they like. And by similarity they downvoted me because a lot despise everything I make. They are attracted for reaching GMs position more than learning anything.
\nMaybe they think it's cool downvoting anonymised. Cool are those that generate material and don't even bother to get upvotes because they have nothing to prove or even been validated by unknown users that we'll never meet.
\nSimon, you've written many relevant, important topics, since you're \"still expert\" on Kaggle, they aren't attracted by what you've written.
\nUnfortunately, many Kagglers are immature and shallow. Some have big issues and I simply won't waste my time trying to understand them all.
\nBy the way, they are doing a Great Downvoting phD. Thank you.
\nAll the best,
\nMarília .
@mpwolke I'd be very interested in any stats you have, like detection rate / tp / fn for ai versus radiologists. I find those stats endlessly interesting and it'd be great to see it in this subject.
\nThe biggest hurdle I see here is getting good datasets for training
", "rawMarkdown": "@mpwolke I'd be very interested in any stats you have, like detection rate / tp / fn for ai versus radiologists. I find those stats endlessly interesting and it'd be great to see it in this subject.\n\nThe biggest hurdle I see here is getting good datasets for training" }, { "id": 2072712, "postDate": "2022-12-22T11:14:08.260Z", "content": "Unfortunately, till now I don't have any dataset with that subject,
\nThough, I'll keep searching it.
I feel u brother. It was a great effort on your part. What some forget is that this is a platform for sharing ideas as well, and not just be on top of the leaderboard or being a GM.
\nPeople like you are an immense help to those like me who have a steep learning curve ahead of us. Please don't get distracted by such actions and keep doing what you are doing.
\nBest Wishes.
Thank you Sandy for the encouragement words.
\nVery likely, in the future we´re going to see competitions predicting things with those kind of subjects.
As I wrote on the topic and on my answer/reply to Simon Crase: Science is objective.
\nIf professionals that provide/predict the features have any prejudice, all the rest will lead to incorrect solutions/results.
\nI've just begun to learn. Additionally, in this wonderful field of Data Science we can learn, daily, fresh things mostly on Kaggle environment since we have beautiful/skilled minds from all over the world to share material.
\nI try to Not waste time with the bad and live with the good things.
\nBest regards,
\nMarília.
You are so right when you say that biases and prejudices creep into the models and predictions. Of late this has become a major area of study to reduce the biases that a model is learning. We, as responsible beings should keep this in mind while giving data for training models. As they say in Data Science, \"Garbage in, garbage out.\"
\nLooking forward to your latest feeds on medicine and other areas of interest. Followed!
", "rawMarkdown": "You are so right when you say that biases and prejudices creep into the models and predictions. Of late this has become a major area of study to reduce the biases that a model is learning. We, as responsible beings should keep this in mind while giving data for training models. As they say in Data Science, \"Garbage in, garbage out.\"\n\nLooking forward to your latest feeds on medicine and other areas of interest. Followed!" } ] } ] } ], "comments": [ { "id": 2065856, "author_name": "", "author_url": "", "post_date": "2022-12-15T06:15:59.513000", "content": "", "votes": -1, "replies": [ { "id": 2066178, "author_name": "Marília Prata", "author_url": "", "post_date": "2022-12-15T13:12:11.330000", "content": "Hi Crase, weka511
\nI received many downvotes here (more than 6).
\nI've only transcribed research material, since researchers are already working with that subject.
\nhttps://www.hindawi.com/journals/cin/2022/6333573/
It's well-known that anyone that is submitted to hormones therapy can suffer consequences of the use of them. Just remember what happened to many elite athletes.
\nIn this case (Breast Cancer), estrogens, which could affect in the future those that are submmited to gender interventions.
\nMy topic is Not advocating for any cause. As I wrote at the beginning: Science is objective. Breast cancer is connected to hormons.
\nBreaking the 4th Wall. Listing reasons for been downvoted:
\nThey are focused on ML and forget to think critically about who is going to feed their Machine to be analysed. In diagnosis, if you don't provide all the features your diagnostic will be wrong. If people that provide the features have any prejudice, all the rest will lead to incorrect solutions/results.
\nThey don't read. Many read on mobiles. Reading only the titles or what is in the Newsfeed. Many just vote who are their equals and who they like. And by similarity they downvoted me because a lot despise everything I make. They are attracted for reaching GMs position more than learning anything.
\nMaybe they think it's cool downvoting anonymised. Cool are those that generate material and don't even bother to get upvotes because they have nothing to prove or even been validated by unknown users that we'll never meet.
\nSimon, you've written many relevant, important topics, since you're \"still expert\" on Kaggle, they aren't attracted by what you've written.
\nUnfortunately, many Kagglers are immature and shallow. Some have big issues and I simply won't waste my time trying to understand them all.
\nBy the way, they are doing a Great Downvoting phD. Thank you.
\nAll the best,
\nMarília .
@mpwolke I'd be very interested in any stats you have, like detection rate / tp / fn for ai versus radiologists. I find those stats endlessly interesting and it'd be great to see it in this subject.
\nThe biggest hurdle I see here is getting good datasets for training
", "votes": 0, "replies": [] }, { "id": 2072712, "author_name": "Marília Prata", "author_url": "", "post_date": "2022-12-22T11:14:08.260000", "content": "Unfortunately, till now I don't have any dataset with that subject,
\nThough, I'll keep searching it.
I feel u brother. It was a great effort on your part. What some forget is that this is a platform for sharing ideas as well, and not just be on top of the leaderboard or being a GM.
\nPeople like you are an immense help to those like me who have a steep learning curve ahead of us. Please don't get distracted by such actions and keep doing what you are doing.
\nBest Wishes.
Thank you Sandy for the encouragement words.
\nVery likely, in the future we´re going to see competitions predicting things with those kind of subjects.
As I wrote on the topic and on my answer/reply to Simon Crase: Science is objective.
\nIf professionals that provide/predict the features have any prejudice, all the rest will lead to incorrect solutions/results.
\nI've just begun to learn. Additionally, in this wonderful field of Data Science we can learn, daily, fresh things mostly on Kaggle environment since we have beautiful/skilled minds from all over the world to share material.
\nI try to Not waste time with the bad and live with the good things.
\nBest regards,
\nMarília.
You are so right when you say that biases and prejudices creep into the models and predictions. Of late this has become a major area of study to reduce the biases that a model is learning. We, as responsible beings should keep this in mind while giving data for training models. As they say in Data Science, \"Garbage in, garbage out.\"
\nLooking forward to your latest feeds on medicine and other areas of interest. Followed!
", "votes": 0, "replies": [] } ] } ] } ], "raw_markdown_by_id": { "2050593": "#Hormones influence on Breast Cancer\n\nScience is objective, since Breast Cancer is related to Hormones (estrogens, known human carcinogens), we should consider hormones. That's one reason why men have less risk. \n\nBesides, hormones therapy on NEXT analysis could be included, since it's used by transgender.\n\n#On this Competition images are only of female patients:\n\n \"The dataset for this challenge contains radiographic breast images of female subjects. \"\nhttps://www.kaggle.com/competitions/rsna-breast-cancer-detection/data\n\n#Estrogen and Progesterone Risks\n\n\"Studies have shown that a woman’s risk of breast cancer is related to the estrogen and progesterone made by her ovaries (known as endogenous estrogen and progesterone). Being exposed for a long time and/or to high levels of these hormones has been linked to an increased risk of breast cancer.\"\n\nhttps://www.cancer.gov/about-cancer/causes-prevention/risk/hormones#:~:text=Studies%20have%20also%20shown%20that,increased%20risk%20of%20breast%20cancer. \n\n#Male risk to breast cancer\n\n\" Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. \"\n\n#Machine Learning and Male Breast Cancer\n\nMachine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset\n\nCitation: Li, J., & Mani, G. (2021). Machine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset. Journal of Student Research, 10(3). https://doi.org/10.47611/jsrhs.v10i3.2199\n\n\" People who are unaware of the potential danger of getting breast cancer like males would not have the medical awareness beforehand for predictions. Therefore, the PLCO ( The Prostate, Lung, Colorectal, and Ovarian) trials dataset consisting of ages, prostate status, marriage status etc. from National Institute of Cancer is used in this research for detection.\"\n\n\" The main purpose of using PLCO test is to discover the potential risk of getting an Male Breast Cancer (MBC) as soon as possible with low cost and easy collection. It is the rarity of MBC that imposes the threat for males who are unaware of the danger. To explore the relatively most suitable models to use for detecting MBC using non-traditional PLCO test dataset, different existing models including decision tree, random forest, DBSCAN, One Class SVM and so on were used to fit the data.\"\n\n\n\" Due to its extremity of imbalance, evaluation comes from the combination of standard accuracy and Area Under the Receiver Operating Characteristics(AUROC) for the overall accuracy of those models mentioned above. K-means and Logistic Regression models performed best with the AUC score of 0.62 and 0.67. Results suggested that more efficient approaches for common male breast cancer diagnosis or more advanced models and algorithms are needed in further study.\n\nhttps://www.jsr.org/hs/index.php/path/article/view/2199\n\n#Breast Tumor Detection and CNN\n\nBreast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches\n\nAuthors: Mohammad Monirujjaman Khan, Tahia Tazin, Mohammad Zunaid Hussain, Monira Mostakim, Taeefur Rehman, Samender Singh, Vaishali Gupta, and Othman Alomeir.\n\nVolume 2022 | Article ID 6333573 | https://doi.org/10.1155/2022/6333573\n\n- Transgender women are more likely than cisgender men to acquire breast cancer.\n\n- Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer.\"\n\n \"A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.\"\n\nhttps://www.hindawi.com/journals/cin/2022/6333573/\n\n#What is the Male Breast Cancer Study?\n\n\"Breast cancer in men is very rare, but around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"\n\n\"The Male Breast Cancer Study was established to pinpoint the precise genetic, environmental and lifestyle causes of breast cancer in men, which will enable us to identify those who are at risk and understand what can be done to lower the chances of developing the disease.\"\n\n\"The study also aims to identify similarities and differences between breast cancer in men and women.\"\n\nhttps://breastcancernow.org/breast-cancer-research/research-projects/our-research-projects/male-breast-cancer-study\n\n#This topic isn't about gender identity. It's about health and Science.\n\nOn the next analysis, maybe we can embrace all genders. Studies have already done it, as I have described above.\n\n\"Breast cancer in men is very rare: Yearly, 2,600 men in the United States are diagnosed with breast cancer. And, around 370 men are diagnosed with the disease every year in the UK, and around 80 men die.\"\n\n\"Transgender and nonbinary people have the same basic health care needs as cisgender people. Trans and nonbinary people always deserve to be treated respectfully when they get health care, whether it’s specific to gender or not.\"\n\nhttps://www.plannedparenthood.org/learn/gender-identity/transgender/what-do-i-need-know-about-trans-health-care", "2065856": "" } }