Logistic regression is used to predict the probability of an event occurring based on multiple predictor variables. This document discusses using logistic regression to predict the risk of coronary heart disease based on factors like smoking, cholesterol levels, body mass index, gender, age, and physical activity. It finds that smoking and high cholesterol levels are the highest risk factors for heart disease. The odds ratio is used to interpret the results, showing smokers have a 2.4 times higher risk than non-smokers. Examples estimate an inactive smoker's risk at 18% over 10 years, versus practically no risk for a healthy older man.
The word ‘stochastic‘ means a system or process linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. In Gradient Descent, there is a term called “batch” which denotes the total number of samples from a dataset that is used for calculating the gradient for each iteration. In typical Gradient Descent optimization, like Batch Gradient Descent, the batch is taken to be the whole dataset. Although using the whole dataset is really useful for getting to the minima in a less noisy and less random manner, the problem arises when our dataset gets big.
Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
The word ‘stochastic‘ means a system or process linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. In Gradient Descent, there is a term called “batch” which denotes the total number of samples from a dataset that is used for calculating the gradient for each iteration. In typical Gradient Descent optimization, like Batch Gradient Descent, the batch is taken to be the whole dataset. Although using the whole dataset is really useful for getting to the minima in a less noisy and less random manner, the problem arises when our dataset gets big.
Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma
This slide will help you to understand the working of logistic regression which is a type of machine learning model along with use cases, pros and cons.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
In this presentation, we approach a two-class classification problem. We try to find a plane that separates the class in the feature space, also called a hyperplane. If we can't find a hyperplane, then we can be creative in two ways: 1) We soften what we mean by separate, and 2) We enrich and enlarge the featured space so that separation is possible.
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma
This slide will help you to understand the working of logistic regression which is a type of machine learning model along with use cases, pros and cons.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
In this presentation, we approach a two-class classification problem. We try to find a plane that separates the class in the feature space, also called a hyperplane. If we can't find a hyperplane, then we can be creative in two ways: 1) We soften what we mean by separate, and 2) We enrich and enlarge the featured space so that separation is possible.
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
Sheet1Score -54321ScoreAccurately described the leader’s style, t.docxedgar6wallace88877
Sheet1Score ->54321ScoreAccurately described the leader’s style, traits and/or behaviors. Fully described. No additional improvement necessary. Mostly described. Only minimal improvement necessary. Moderately described. Improvement necessary. Minimally described. Room for significant improvement. Did not accurately describe. Applied course material to what you learned about the leader. Fully applied. No further Improvement necessary. Mostly applied. Only minimal improvement necessary. Moderately applied. Improvement necessary. Minimally applied. Room for significant improvement. Did not apply course material. Used citations from the week’s reading materials. Fully cited course materials. No further improvement necessary. Mostly cited course materials. Only minimal improvement necessary. Moderately cited course materials. Improvement necessary. Minimally cited. Room for significant improvement. Did not cite appropriately. Wrote with sufficient detail. Fully detailed. No further improvement necessary. Mostly detailed. Only minimal improvement necessary. Moderately detailed. Improvement necessary. Minimal detail. Room for significant improvement. Did not provide sufficient detail. Used appropriate grammar, punctuation and masters-level writing style Fully used appropriate writing style. No further improvements necessary. Mostly used appropriate writing style. Only minimal improvement necessary. Moderately used appropriate writing style. Improvement necessary. Minimally used appropriate writing style. Room for significant improvement. Did not use appropriate writing style. Final Score0
After reading Chapter 9 of Epidemiology for public health practice, complete Study Questions and Exercises 1–9. This activity is located on pages 431–432. Submit your responses in the form of a Word document.
1- Calculate the etiologic fraction when the RR for disease associated with a given exposure is 1.2, 1.8, 3, and 15.
2- The impact of an exposure on a population does not depend upon:
· a.the strength of the association between exposure and disease.
· b.the prevalence of the exposure.
· c.the case fatality rate.
· d.the overall incidence rate of disease in the population.
· The next seven questions (3–9) are based on the following data: The death rate per 100,000 for lung cancer is 7 among nonsmokers and 71 among smokers. The death rate per 100,000 for coronary thrombosis is 422 among nonsmokers and 599 among smokers. The prevalence of smoking in the population is 55%. (If necessary, refer to the chapter on cohort studies for formulas for RR.)
3- What is the RR of dying of lung cancer for smokers versus nonsmokers?
4-What is the RR of dying of coronary thrombosis for smokers versus nonsmokers?
5-What is the etiologic fraction of disease due to smoking among individuals with lung cancer?
6-What is the etiologic fraction of disease due to smoking among individuals with coronary thrombosis?
7-What is the population etiologic fraction of lung c.
The learning speed of the feed forward neural
network takes a lot of time to be trained which is a major
drawback in their applications since the past decades. The
key reasons behind may be due to the slow gradient-based
learning algorithms which are extensively used to train the
neural networks or due to the parameters in the networks
which are tuned iteratively using some learning algorithms.
Thus, in order to eradicate the above pitfalls, a new learning
algorithm was proposed known as Extreme Learning Machines
(ELM). This algorithm tries to compute Hidden-layer-output
matrix that is made of randomly assigned input layer and
hidden layer weights and randomly assigned biases. Unlike the
other feedforward networks, ELM has the access of the whole
training dataset before going into the computation part. Here,
we have devised a new two-layer-feedforward network (TFFN)
for ELM in a new manner with randomly assigning the weights
and biases in both the hidden layers, which then calculates the
output-hidden layer weights using the Moore-Penrose generalized
inverse. TFFN doesn’t restricts the algorithm to fix the number
of hidden neurons that the algorithm should have. Rather it
searches the space which gives an optimized result in the neurons
combination in both the hidden layers. This algorithm provides a
good generalization capability than the parent Extreme Learning
Machines at an extremely fast learning speed. Here, we have
experimented the algorithm on various types of datasets and
various popular algorithm to find the performances and report
a comparison.
Assessment of the Prevalence of Some Cardiovascular Risk Factors among the O...Scientific Review SR
The prevalence of some cardiovascular risk factors among the Ogonis and Ikwerres in Rivers State,
Nigeria was assessed in two hundred subjects. Well structured questionnaires were used to assess smoking status,
duration of diabetes, age, weight, and height from the participants. Measurement of blood pressure was done to
ascertain the blood pressure of the subjects. Analysis of fasting blood sugar was done to confirm diabetes s tatus of
participants. Body mass index (BMI), was calculated from the height and weight. The mean age of males in the
study was higher than that of the females (P=.05). Mean SBP and DBP values were significantly higher (P=.05)
among the Ikwerres and Ogonis. BMI was significantly higher for Ogonis than Ikwerres ( P=.05). In the various
categories of risk, BMI for males was diabetics (47.89), smokers (44.73) and hypertensives (45.37) for type III
obesity which shows a higher risk for cardiovascular disease.
Analytic StudiesThere are basically two types of studies experi.docxrossskuddershamus
Analytic Studies
There are basically two types of studies: experimental and observational. In an experimental study, the exposure has not occurred yet. The investigator controls the exposure in the study groups and studies the impact. For example, he may immunize one group with an experimental vaccine that has been developed for a disease and compare the frequency with which the disease develops to the control group (which had no modification). In an observational study, the exposure has already occurred. The exposures and outcomes are observed and analyzed, not created experimentally. Observational studies are often more practical and continue to provide the major contribution to our understanding of diseases. There are two main types of observational studies: cohort (prospective) and case-control (retrospective) studies.
In a cohort study, a group of people who share a common experience within a defined time period (cohort) are categorized based upon their exposure status. For example, individuals at a work place where an asbestos exposure occurred would be considered a cohort. Another example would be individuals attending a wedding where a foodborne illness occurred. Cohort studies have well-defined populations. Often, cohort studies involve following a cohort over time in order to determine the rate at which a disease develops in relation to the exposure.
In a cohort study, relative risk is used to determine whether an association exists between an exposure and a disease. Relative risk is defined as ratio of the incidence rate among exposed individuals to the incidence rate among unexposed individuals.
To calculate the relative risk, you would use the following formula: (a/a+b) / (c/c+d) where:
a = the number of individuals with a disease who were exposed.
b = the number of individuals without a disease who were exposed.
c = the number of individuals with a disease who were NOT exposed.
d = the number of individuals without a disease who were NOT exposed.
In a case-control study, the sample is based upon illness status, rather than exposure status. The researcher identifies a group of people who meet the case definition and a group of people who do not have the illness (controls). The objective is to determine if the two groups differ in the rate of exposure to a specific factor or factors.
In contrast to a cohort study, the total number of people exposed in a case-control study is unknown. Therefore, relative risk cannot be used. Instead, an odds ratio or risk ratio is used. An odds ratio measures the odds that an exposed individual will develop a disease in comparison to an unexposed individual. Please click the button below to learn how to calculate an odds ratio.
To calculate an odds ratio, you would use the following formula: ad/bc
where:
a = the number of individuals with a disease who were exposed.
b = the number of individuals without a disease who were exposed.
c = the number of individu.
Excelsior College PBH 321 Page 1 CONFOUNDING .docxgitagrimston
Excelsior College PBH 321
Page 1
CONFOUNDING
Confounding is a mixing of effects of extraneous factors (confounders) with the effect of the
exposure of interest. The association between exposure and disease is distorted because it is
mixed with the effect of another factor that is associated with the disease. A confounder is
therefore an alternate explanation for observed association between an exposure and disease.
The result of confounding is to distort the true association toward or away from the null. Many
epidemiologists refer to confounding as a type of bias.
Example: Who can run faster, men or women?
Exposure: gender
Outcome: speed
Hypothesis: The average running speed of men is faster than the average running speed of
women.
All men and women in one town were invited to participate in a road race. On race day,
both men and women come and race. The average running time for the men is faster than
the women. CONCLUSION: Men run faster than women, because of their gender.
But wait! Someone notices that women with young children did not race. In fact, women
who ran the race were, on average, older than men who ran. For example, the average age
of women was 50 years while the average age of men was 25 years. CONCLUSION: Perhaps
men were faster not because of their gender, but because they were younger.
Another race is held, this time making sure ages in the two groups (men and women) are
comparable. That is, the men and women have the same distribution of ages. Race result:
Once again, men are faster. CONCLUSION: Controlling for age, men are still faster than
women.
But wait! Someone points out that the men are, on average, taller than the women.
CONCLUSION: Perhaps men were faster not due to their gender, but because their legs are
longer.
Another race is held, this time making sure both heights and ages in the two groups (men
and women) are comparable. Race result: Once again, men are faster.
But wait! Someone points out that 50% of the women had hair longer than their shoulders,
and only 5% of the men did! CONCLUSION: Long hair made the women run slower? (Is this
a reasonable conclusion?)
Excelsior College PBH 321
Page 2
CRITERIA FOR CONFOUNDING
Let’s review the meaning of association. If a characteristic is associated with disease, then risk
of disease is different among people with the characteristic compared to those without. If the
characteristic is associated with exposure, then the distribution of the characteristic is different
among people with the exposure compared to people without exposure (unbalanced between
groups).
In general, for a characteristic to be a confounder, it must be associated with both the outcome
and the exposure under study. (Think about the race example: Why would age and height be
reasonable explanations, but not hair length?)
There are three major criteria that must be satisfied for a factor to be a confounde ...
PREVENTION OF HEART PROBLEM USING ARTIFICIAL INTELLIGENCEijaia
Heart is the most important organ of a human body as it not only circulates oxygen and other vital
nutrients through blood to different parts of the body and helping in the metabolic activities but also
removes metabolic wastes. Thus, even a minor problem can affect the whole organism. Hence, researchers
are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an
analysis of the data related to different health problems and its functioning can help in predicting with a
certain probability for the wellness of this organ. In this paper we have analysed the different prescribed
data of patients from different parts of India. Using this data, we have built a model which gets trained
using this data and tries to predict whether a new out-of-sample data has a probability of having any heart
attack or not. This model can help in the decision making along with the doctor to treat the patient well and
creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only
the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along
with the AUC-ROC helps in building/fixing the algorithm inside the model.
MIRA Risk Review: Multivariate metabolic risk calculatorMunich Re
Worldwide, cardiovascular diseases (CVDs) are among the most widespread causes of death. They also play an important role in morbidity and disability. Providing unprecedented detail and precision, the highly sophisticated multivariate metabolic risk calculator looks at all four main cardiovascular risk factors excessive weight, blood pressure, blood lipids and blood glucose – as well as their correlation and interaction. This MIRA Risk Review paper looks at the multivariate metabolic risk calculator, a new methodology that allows for unprecedented accuracy.
Munich Re’s internet-based underwriting tool MIRA is a high-performance integrated solution that fits seamlessly into your workflow. MIRA gives you instant access to a vast and continuously evolving pool of rating recommendations as well as interactive support from Munich Re underwriting experts – and enables you to process and store the resulting documentation in your own data infrastructure.
For more information on MIRA, visit: http://bit.ly/MIRA-Risk-Review
Diagnosis of Early Risks, Management of Risks, and Reduction of Vascular Dise...asclepiuspdfs
In a recent issue of the Journal of Circulation, American Heart Association has published a scientific statement, related to the excess heart disease and acute vascular events in South Asians living in the USA. The same group of experts, also have published a complementary article in Circulation titled, “call to action: Cardiovascular disease (CVD) in Asian Americans.”I being a South Asian immigrant living in the USA, have always wondered as to why we do not have the same benefits as the other resident Americans in terms of the advantages of living in a highly advanced country? According to a study done in 2013, cardiovascular mortality has declined and diabetes mortality has increased in high-income countries. The study done in 26 industrialized nations, estimated the potential role of trends in population, for body mass index, systolic blood pressure, serum total cholesterol, and smoking, the modifiable risk factors identified as the promoters of CVD, and acute vascular events, by the Framingham Heart Study (FHS) group.
Excelsior College PBH321 1 Confounding .docxgitagrimston
Excelsior College PBH321
1
Confounding is a mixing of effects of extraneous factors (confounders) with the effect of the exposure of
interest. The association between exposure and disease is distorted because it is mixed with the effect
of another factor that is also associated with the disease. A confounder is therefore an alternative
explanation for the observed association between an exposure and disease. The result of confounding is
to distort the true association either towards or away from the null. Many epidemiologists refer to
confounding as a type of bias.
Example: Who can run faster, men or women?
Exposure: gender
Outcome: speed
Hypothesis: The average running speed of men is faster than the average running speed of women.
All men and women in one town invited to participate in a road race. On race day, both men and
women come and race. The average running time for the men is faster than the women.
CONCLUSION: Men run faster than women, because of their gender.
But wait! Someone notices that women with young children did not race. In fact, women who ran
the race were, on average, older than men who ran. For example, the average age of women was 50
years while the average age of men was 25 years. CONCLUSION: Perhaps men were faster not
because of their gender, but because they were younger.
Another race is held, this time making sure that the ages in the two groups (men and women) are
comparable. In other words, the men and women have same distribution of ages. Race result: Once
again, men are faster. CONCLUSION: Controlling for age, men are still faster than women.
But wait! Someone points out that the men are, on average, taller than the women. CONCLUSION:
Perhaps men were faster not due to their gender, but because their legs are longer.
Another race is held, this time making sure both heights and ages in the two groups (men and
women) are comparable. Race result: Once again, men are faster. CONCLUSION: Men are faster
than women, regardless of age or height.
But wait! Someone points out that 50% of the women had hair longer than their shoulders, and
only 5% of the men did! CONCLUSION: Long hair made the women run slower? (Is this a reasonable
conclusion?)
The point of this exercise is to demonstrate that there are in fact often many alternative explanatory
factors for the association between an exposure and an outcome. Properly considering potential
confounding factors is an important part of any epidemiologic analysis.
Criteria for confounding
Let’s review the meaning of association. If a characteristic is associated with disease, then the risk of
disease is different among people with the characteristic compared to those without. If the
CONFOUNDING
Excelsior College PBH321
2
characteristic is associated with exposure, then the distribution of the characteristic is different among
people with the exposure compared to people witho ...
HEALTH SCREENING SERVICES IN COMMUNITY PHARMACY .pptxLipanjali Badhei
Content:
INTRODUCTION
SCOPE
IMPORTANCE OF HEALTH SCREENING
SUCCESS OF HEALTH SCREENING
TYPES OF HEALTH SCREENING
ROUTINE Monitoring OF PATIENT
EARLY DISEASE DETECTION
SOME DISEASE AND THEIR HEALTH SCREENING SERVICE
20 key strategies to a healthier heart - Karmic Ally CoachingVatsala Shukla
Considering your heart health and taking care of your heart is extremely important since it is the #1 killer of both men and women in the United States and even worldwide. This short report gives your 20 key strategies to a healthier heart.
Social media network maps visualize the patterns of connection that form when people follow, reply and mention one another in Internet communication services like Twitter. When analyzed in aggregate collections of individual connections form web-like network structures.
As presented at the CASRO Digital Research Conference by Michael Lieberman of Multivariate Solutions.
Giving You the Edge - The Science of Winning Elections Michael Lieberman
Giving You the Edge – The Science of Winning Elections, written by experienced political consultant Michael Lieberman, identifies and explains the use of key research methodology and multivariate analysis in supporting political campaign goals through the various stages of an election.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
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.
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.
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.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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
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.
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.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
5. Logistic Regression Output This slide is descriptive, and shows which of the variables are most influential in determining which risk factor is most relevant when considering Coronary Heart Disease. For example, smoking and a total cholesterol level above 200 are the highest risk factors. When examining the results, the Odds-Ratio is often used to interpret the results. Smokers' risk of developing coronary heart disease is 2.4 times that of nonsmokers. High cholesterol is also a risk factor, as is age. That men are slightly more likely to get Coronary Heart Disease than women, and that physical activity sharply reduces the chances of Coronary Heart Disease (negative coefficient).
6.
7. Example One Inactive, Smoking, 55-year-old Woman A slightly obese, 55-year-old woman, smoker, with somewhat high total cholesterol and is physically inactive has an 18% chance of contracting Coronary Heart Disease within the next ten years.
8. Example Two Health-Conscience 65-Year-Old Man Using the logistic output, the chances of a non-smoking, physically active 65-year-old man with a good cholesterol level has practically no chance of contracting Coronary Heart Disease in the next ten years.
Editor's Notes
1 National Jewish Outreach Hebrew Reading Crash Course 1
Logistic Regression Analysis was introduced by the US physicist, Joseph Berkson
This second example is quite different in nature. And the difference lies in the characteristics of the variables we’re using. We want to explain the outcome, i.e.: passing or failing the exam with our explanatory variable: the number of hours of study. In this case, the dependent variable can take just two possible values: 1 if the student passes, 0 if the student fails. We will call this type of variable ‘dichotomic’ or ‘binary’ variable.
Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.