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==Applications==
Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score ([[TRISS]]), which is widely used to predict mortality in injured patients, was originally developed by Boyd ''{{Abbr|et al.|''et alia'', with others - usually other authors}}'' using logistic regression.<ref>{{cite journal| last1 = Boyd | first1 = C. R.| last2 = Tolson | first2 = M. A.| last3 = Copes | first3 = W. S.| title = Evaluating trauma care: The TRISS method. Trauma Score and the Injury Severity Score| journal = The Journal of Trauma| volume = 27 | issue = 4| pages = 370–378| year = 1987 | pmid = 3106646 | doi= 10.1097/00005373-198704000-00005| doi-access = free}}</ref> Many other medical scales used to assess severity of a patient have been developed using logistic regression.<ref>{{cite journal |pmid= 11268952 |year= 2001|last1= Kologlu |first1= M.|title=Validation of MPI and PIA II in two different groups of patients with secondary peritonitis |journal=Hepato-Gastroenterology |volume= 48 |issue=37 |pages= 147–51 |last2=Elker|first2=D. |last3= Altun |first3= H. |last4= Sayek |first4= I.}}</ref><ref>{{cite journal |pmid= 11129812 |year= 2000 |last1= Biondo |first1= S. |title= Prognostic factors for mortality in left colonic peritonitis: A new scoring system |journal= Journal of the American College of Surgeons|volume= 191 |issue= 6 |pages= 635–42 |last2= Ramos|first2=E.|last3=Deiros |first3= M. |last4=Ragué|first4=J. M.|last5=De Oca |first5= J. |last6= Moreno |first6=P.|last7=Farran|first7=L.|last8= Jaurrieta |first8= E. |doi= 10.1016/S1072-7515(00)00758-4}}</ref><ref>{{cite journal|pmid=7587228 |year= 1995 |last1=Marshall |first1= J. C.|title=Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome|journal=Critical Care Medicine|volume= 23 |issue= 10|pages= 1638–52 |last2= Cook|first2=D. J.|last3=Christou|first3=N. V. |last4= Bernard |first4= G. R. |last5=Sprung|first5=C. L.|last6=Sibbald|first6=W. J.|doi= 10.1097/00003246-199510000-00007}}</ref><ref>{{cite journal|pmid=8254858|year=1993 |last1= Le Gall |first1= J. R.|title=A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study|journal=JAMA|volume=270|issue= 24 |pages= 2957–63 |last2= Lemeshow |first2=S.|last3=Saulnier|first3=F.|doi= 10.1001/jama.1993.03510240069035}}</ref> Logistic regression may be used to predict the risk of developing a given disease (e.g. [[Diabetes mellitus|diabetes]]; [[Coronary artery disease|coronary heart disease]]), based on observed characteristics of the patient (age, sex, [[body mass index]], results of various [[blood test]]s, etc.).<ref name = "Freedman09">{{cite book |author=David A. Freedman |year=2009|title=Statistical Models: Theory and Practice |publisher=[[Cambridge University Press]]|page=128|author-link=David A. Freedman}}</ref><ref>{{cite journal | pmid = 6028270▼
=== General ===
▲Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score ([[TRISS]]), which is widely used to predict mortality in injured patients, was originally developed by Boyd ''{{Abbr|et al.|''et alia'', with others - usually other authors}}'' using logistic regression.<ref>{{cite journal| last1 = Boyd | first1 = C. R.| last2 = Tolson | first2 = M. A.| last3 = Copes | first3 = W. S.| title = Evaluating trauma care: The TRISS method. Trauma Score and the Injury Severity Score| journal = The Journal of Trauma| volume = 27 | issue = 4| pages = 370–378| year = 1987 | pmid = 3106646 | doi= 10.1097/00005373-198704000-00005| doi-access = free}}</ref> Many other medical scales used to assess severity of a patient have been developed using logistic regression.<ref>{{cite journal |pmid= 11268952 |year= 2001|last1= Kologlu |first1= M.|title=Validation of MPI and PIA II in two different groups of patients with secondary peritonitis |journal=Hepato-Gastroenterology |volume= 48 |issue=37 |pages= 147–51 |last2=Elker|first2=D. |last3= Altun |first3= H. |last4= Sayek |first4= I.}}</ref><ref>{{cite journal |pmid= 11129812 |year= 2000 |last1= Biondo |first1= S. |title= Prognostic factors for mortality in left colonic peritonitis: A new scoring system |journal= Journal of the American College of Surgeons|volume= 191 |issue= 6 |pages= 635–42 |last2= Ramos|first2=E.|last3=Deiros |first3= M. |last4=Ragué|first4=J. M.|last5=De Oca |first5= J. |last6= Moreno |first6=P.|last7=Farran|first7=L.|last8= Jaurrieta |first8= E. |doi= 10.1016/S1072-7515(00)00758-4}}</ref><ref>{{cite journal|pmid=7587228 |year= 1995 |last1=Marshall |first1= J. C.|title=Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome|journal=Critical Care Medicine|volume= 23 |issue= 10|pages= 1638–52 |last2= Cook|first2=D. J.|last3=Christou|first3=N. V. |last4= Bernard |first4= G. R. |last5=Sprung|first5=C. L.|last6=Sibbald|first6=W. J.|doi= 10.1097/00003246-199510000-00007}}</ref><ref>{{cite journal|pmid=8254858|year=1993 |last1= Le Gall |first1= J. R.|title=A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study|journal=JAMA|volume=270|issue= 24 |pages= 2957–63 |last2= Lemeshow |first2=S.|last3=Saulnier|first3=F.|doi= 10.1001/jama.1993.03510240069035}}</ref> Logistic regression may be used to predict the risk of developing a given disease (e.g. [[Diabetes mellitus|diabetes]]; [[Coronary artery disease|coronary heart disease]]), based on observed characteristics of the patient (age, sex, [[body mass index]], results of various [[blood test]]s, etc.).<ref name
| year = 1967
| last1 = Truett
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| pages = 511–24
| last2 = Cornfield| first2 = J| last3 = Kannel| first3 = W | doi= 10.1016/0021-9681(67)90082-3}}</ref> Another example might be to predict whether a Nepalese voter will vote Nepali Congress or Communist Party of Nepal or Any Other Party, based on age, income, sex, race, state of residence, votes in previous elections, etc.<ref name="rms" /> The technique can also be used in [[engineering]], especially for predicting the probability of failure of a given process, system or product.<ref name=
=== Supervised machine learning ===
Logistic regression is a [[supervised machine learning]] algorithm widely used for [[binary classification]] tasks, such as identifying whether an email is spam or not and diagnosing diseases by assessing the presence or absence of specific conditions based on patient test results. This approach utilizes the logistic (or sigmoid) function to transform a linear combination of input features into a probability value ranging between 0 and 1. This probability indicates the likelihood that a given input corresponds to one of two predefined categories. The essential mechanism of logistic regression is grounded in the logistic function's ability to model the probability of binary outcomes accurately. With its distinctive S-shaped curve, the logistic function effectively maps any real-valued number to a value within the 0 to 1 interval. This feature renders it particularly suitable for binary classification tasks, such as sorting emails into "spam" or "not spam". By calculating the probability that the dependent variable will be categorized into a specific group, logistic regression provides a probabilistic framework that supports informed decision-making.<ref>{{Cite web |title=Logistic Regression |url=https://www.mastersindatascience.org/learning/machine-learning-algorithms/logistic-regression/ |access-date=2024-03-16 |website=CORP-MIDS1 (MDS) |language=en-US}}</ref>
==Example==
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