%0 Journal Article %1 noauthororeditor %A Prudvi, Puram Surya %A Sharifahmadian, Ershad %D 2017 %J International Journal of Computer Science, Engineering and Applications (IJCSEA) %K algorithms failure networks speech system %N 05 %P 09 %R 10.5121/ijcsea.2017.7501 %T APPLYING MACHINE LEARNING TECHNIQUES TO FIND IMPORTANT ATTRIBUTES FOR HEART FAILURE SEVERITY ASSESSMENT %U http://aircconline.com/ijcsea/V7N5/7517ijcsea01.pdf %V 07 %X The diagnosis of heart disease depends mostly on the combination of clinical and pathological data. It leads to the quality of medical care provided for the patient. In this paper, three machine learning (ML) techniques −Classification and Regression tree (CART), Neural Networks (NN), and Support vector machine (SVM)− are utilized to find the best attributes for estimating the severity of heart failure. The data is collected from three different resources, then each input attribute used for assessing the severity of heart failure is analyzed individually after implementing the machine learning techniques. Finally, the most important supportive attributes are presented in this paper by which medical staffs can identify heart failure severity fast and more accurately. In fact, by screening important attributes, clinicians can make better decision about right treatment procedures or preventive actions that reduce risk of heart attacks.

@article{noauthororeditor, abstract = {The diagnosis of heart disease depends mostly on the combination of clinical and pathological data. It leads to the quality of medical care provided for the patient. In this paper, three machine learning (ML) techniques −Classification and Regression tree (CART), Neural Networks (NN), and Support vector machine (SVM)− are utilized to find the best attributes for estimating the severity of heart failure. The data is collected from three different resources, then each input attribute used for assessing the severity of heart failure is analyzed individually after implementing the machine learning techniques. Finally, the most important supportive attributes are presented in this paper by which medical staffs can identify heart failure severity fast and more accurately. In fact, by screening important attributes, clinicians can make better decision about right treatment procedures or preventive actions that reduce risk of heart attacks. }, added-at = {2018-04-05T12:13:01.000+0200}, author = {Prudvi, Puram Surya and Sharifahmadian, Ershad}, biburl = {https://www.bibsonomy.org/bibtex/24625180c727f4bd4bffb0b85e8ff91f0/ijcsea}, doi = {10.5121/ijcsea.2017.7501}, interhash = {15a31175da6fa9bf949a15521028dcb5}, intrahash = {4625180c727f4bd4bffb0b85e8ff91f0}, issn = {ISSN 2230: 9616}, journal = {International Journal of Computer Science, Engineering and Applications (IJCSEA)}, keywords = {algorithms failure networks speech system}, month = oct, number = 05, pages = 09, timestamp = {2018-04-05T12:13:01.000+0200}, title = {APPLYING MACHINE LEARNING TECHNIQUES TO FIND IMPORTANT ATTRIBUTES FOR HEART FAILURE SEVERITY ASSESSMENT}, url = {http://aircconline.com/ijcsea/V7N5/7517ijcsea01.pdf}, volume = 07, year = 2017 }