IJERT-Comparitive Analysis of Machine Learning Algorithms in the Study of Heart Disease Prediction (original) (raw)
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IJERT-Heart Disease Prediction using Machine learning and Data Mining Technique
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/heart-disease-prediction-using-machine-learning-and-data-mining-technique https://www.ijert.org/research/heart-disease-prediction-using-machine-learning-and-data-mining-technique-IJERTCONV9IS03065.pdf Nowadays Heart disease is considered one of the major causes in today's world. It cannot be easily predicted by the medical doctors as it is a difficult task that demands expertise and higher knowledge for prediction. There is a lot of data available within the healthcare systems on the internet. However, there is a lack of effective analysis tools to discover hidden relationships and patterns in data. An automated system in medical diagnosis would enhance medical efficiency and reduce costs. This web application intends to predict the occurrence of a disease based on data gathered from medical research particularly in Heart Disease. The goal is to extract the hidden patterns by applying data mining techniques on the dataset, which are noteworthy to heart diseases and to predict the presence of heart disease in patients where the presence is valued on a scale. The prediction of heart disease requires a huge size of data which is too complex and massive to process and analyze by conventional techniques. Our objective is to find out the suitable machine learning technique that is computationally efficient as well as accurate for the prediction of heart disease.
Prediction of Heart Disease Using Machine Learning Algorithm
2018
Data mining techniques have been applied magnificently in many fields including business, science, the Web, cheminformatics, bioinformatics, and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop a software with the help machine learning algorithm which can help doctors to take decision regarding both prediction and diagnosing of heart disease. The main objective of this research paper is predicting the heart disease of a patient using machine learning algorithms.
IRJET, 2021
Nowadays, heart disease is one of the prevailing main causes of morbidity and mortality. It is a hot health topic in our daily life, and heart disease treatment is very complicated. It is one-third of all deaths globally, stroke and heart disease. They both are globally the biggest killer, and their diagnosis availability is infrequent, especially in developing countries. This paper contains a framework based on some machine learning and data mining classification techniques on the heart disease dataset. There is no operational use of the data produced from the hospitals. Some convinced tools are used to extract the facts from the database to recognize the heart. This work is done by using Cleveland heart disease dataset that is sourced from the "UCI Machine Learning (ML) repository" to test and analyze on some various supervised ML and data mining techniques, some different attributes associated with causing of cardiovascular heart disease age, sex, chest pain type, chol, thal, etc. We will use these respective data to a model that will predict whether the patient has heart disease or not. This paper discussed the results of the modern techniques and will be used to predict the results for heart disease by summarizing some current research. The proposed method works best result in 86.89% accuracy by using a logistic regression algorithm.
Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms
Engineering, MAthematics and Computer Science (EMACS) Journal
Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.Heart disease is inflammation or damage to the heart and blood vessels over time. the disease c...
Heart Disease Prediction using Machine Learning Techniques Comparative Study
Soft Computing Research Society eBooks, 2021
Now a days there is a significant amount of information is present in across the internet in the form of various types more specifically the information on, websites, news, blogs and other digital content. But there is valuable or meaningful information which is hidden inside the data which is very crucial for taking many important decisions. Thus this research is very useful for obtaining such useful information from the available one. The best tool for the extracting the useful information from the available large amount of information is known as data mining. This research deals to get the useful information from large amount of data and which is used in taking the crucial decision. Data mining is the one of the important tool to extract useful and meaningful information from the available large amount of data. Hence data mining is used in most of the applications like healthcare, whether forecasting and entertainment. The importance of data mining in the field of healthcare has proven its importance particularly in preventing, predicting and detecting and also in curing most of the heart diseases should be considered as milestone.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
We are living in a post modern era and there are tremendous changes happening to our daily life which make an impact on our health positively and negatively. As a result of these changes various kind of diseases are enormously increased. In the medical field, the diagnosis of cardiovascular disease is the most difficult task. The diagnosis of cardiovascular disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal source of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. This research paper intends to provide a survey of techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Cardiovascular Disease Prediction.
International Journal of Advanced Computer Science and Applications
Heart diseases are considered one of the leading causes of death globally over the world. They are difficult to be predicted by a specialist physician as it is not an easy task which requires greater knowledge and expertise for prediction. With the variety of machine learning and deep learning algorithms, there exist many recent studies in the state of the art that have been done remarkable and practical works for predicting the presence of heart diseases. However, some of these works were affected by various drawbacks. Hence, this work aims to compare and analyze different classifiers, pre-processing, and dimensionality reduction techniques (feature selection and feature extraction) and study their effect on the prediction of heart diseases existence. Therefore, based on the resulting performance of several conducted experiments on the well-known Cleveland heart disease dataset, the findings of this study are: 1) the most significant subset of features to predict the existence of heart diseases are PES, EIA, CPT, MHR, THA, VCA, and OPK, 2) Naïve Bayes classifier gave the best performance prediction, and 3) Chi-squared feature selection was the data mining technique that reduced the number of features while maintained the same improved performance for predicting the presence of heart disease.
Heart Disease Prediction Using Machine Learning Algorithm
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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...