Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

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Intrusion Detection Using Feature Selection and Machine Learning Algorithm with Misuse Detection

International Journal of Computer Science and Information Technology ◽

10.5121/ijcsit.2016.8102 ◽

2016 ◽

Vol 8 (1) ◽

pp. 17-25 ◽

Cited By ~ 6

Author(s):

Harvinder Pal Singh Sasan ◽

Meenakshi Sharma

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intrusion Detection ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Misuse Detection

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Classification of Diabetes using Random Forest with Feature Selection Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3595.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1295-1300 ◽

Cited By ~ 1

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Electronic Health Records ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Health Records

Diabetes has become a serious problem now a day. So there is a need to take serious precautions to eradicate this. To eradicate, we should know the level of occurrence. In this project we predict the level of occurrence of diabetes. We predict the level of occurrence of diabetes using Random Forest, a Machine Learning Algorithm. Using the patient’s Electronic Health Records (EHR) we can build accurate models that predict the presence of diabetes.

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Heart Disease Prediction Using Machine Learning Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9340.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 2603-2606

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Decision Tree ◽

Learning Algorithm ◽

Heart Diseases ◽

Support Vector ◽

Machine Learning Algorithm ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Tree Classifier

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 basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients

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