Bayseian Classifier Implementation
PATIENT’S SURVIVAL ANALYSIS AFTER A HEART ATTACK (TRAUMA)
Ravinder pal Singh Id-6405231 mailme2rv@gmail.com Harsimran Jit Singh Mutti Id-6241905
harsimranmutti@yahoo.com
ABSTRACT
In data mining, classification is a form of data analysis that can be used to extract models describing important data classes and it predicts categorical class labels and classifies data. There are many algorithms which are used in classification i.e. ID3, C4.5, Apriori, FP-growth, Back propagation Neural Network (BNN) and Naïve Bayes (NB). Bayes data mining technique are a fundamentally important technique. Bayes theorem finds the event occurring probability given the probability of another already occurred event. Bayes Rule is applied for calculating the posterior from the prior and the likelihood, due to the later two is generally easier to be generated from a probability model. Statistics provide a strong fundamental background to quantify and evaluate the results. However, algorithms based on statistics need to be modified and refined before they are applied to data mining. General terms Keywords Data mining, knowledge base, Naive Bayesian Classifier, Bayesian theorem
from the huge amount of data, which can be utilized for future prediction or intelligence and also for knowledge discovery. There are many applications of data mining techniques in various fields such as engineering, medical, financial, and business. Here we have discussed application of classification algorithm which is an important part of the data mining algorithms into medical field.
Motivation and Real world scenario
With the increasing innovation power, data-mining methods helps us to reduce the risks associated with conducting any particular task and improve decision-making. Especially here we have applied data mining
Ravinder pal Singh Id-6405231 mailme2rv@gmail.com Harsimran Jit Singh Mutti Id-6241905
harsimranmutti@yahoo.com
ABSTRACT
In data mining, classification is a form of data analysis that can be used to extract models describing important data classes and it predicts categorical class labels and classifies data. There are many algorithms which are used in classification i.e. ID3, C4.5, Apriori, FP-growth, Back propagation Neural Network (BNN) and Naïve Bayes (NB). Bayes data mining technique are a fundamentally important technique. Bayes theorem finds the event occurring probability given the probability of another already occurred event. Bayes Rule is applied for calculating the posterior from the prior and the likelihood, due to the later two is generally easier to be generated from a probability model. Statistics provide a strong fundamental background to quantify and evaluate the results. However, algorithms based on statistics need to be modified and refined before they are applied to data mining. General terms Keywords Data mining, knowledge base, Naive Bayesian Classifier, Bayesian theorem
from the huge amount of data, which can be utilized for future prediction or intelligence and also for knowledge discovery. There are many applications of data mining techniques in various fields such as engineering, medical, financial, and business. Here we have discussed application of classification algorithm which is an important part of the data mining algorithms into medical field.
Motivation and Real world scenario
With the increasing innovation power, data-mining methods helps us to reduce the risks associated with conducting any particular task and improve decision-making. Especially here we have applied data mining
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