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International Journal of Data Mining & Knowledge Management Process , 2023
Heart disease is most common disease reported currently in the United States among both the gende... more Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.
International Journal of Innovative Technology and Exploring Engineering, 2019
Heart disease is a common problem which can be very severe in old ages and also in people not hav... more Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basi...
Ocimum basilicum attenuates ethidium bromide-induced cognitive deficits and pre-frontal cortical neuroinflammation, astrogliosis and mitochondrial dysfunction in rats
Metabolic Brain Disease
Strata behavior in longwall mining at greater depths
Strata behavior in longwall mining at greater depths
International Journal of Data Mining & Knowledge Management Process , 2023
Heart disease is most common disease reported currently in the United States among both the gende... more Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.
International Journal of Innovative Technology and Exploring Engineering, 2019
Heart disease is a common problem which can be very severe in old ages and also in people not hav... more Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basi...
Ocimum basilicum attenuates ethidium bromide-induced cognitive deficits and pre-frontal cortical neuroinflammation, astrogliosis and mitochondrial dysfunction in rats
Metabolic Brain Disease
Strata behavior in longwall mining at greater depths
Strata behavior in longwall mining at greater depths