Screening of Ischemic Heart Disease based on PPG Signals using Machine Learning Techniques

Heart disease is the leading cause of death from chronic diseases in the developing countries. The difficulty of making an accurate and timely diagnosis is exacerbated by a lack of resources and professionals in some areas, which contributes to this reality. Medical professionals may benefit from technological advancements that aid in the accurate diagnosis of patients. In light of these findings, a hybrid diagnostic tool has been developed that combines several computational intelligence (machine learning) techniques capable of analyzing clinical histories and images of electrocardiogram signals and indicating whether or not the patient has ischemic heart disease with up to 97.01% accuracy. Working with medical experts and a database containing clinical data on approximately 1020 patients and their diagnoses was required for this project. Both were put to use. A picture database containing 92 images of electrocardiogram signals was also used in this project for the analysis of the Artificial Neural Network. After extensive research and testing by the medical community, which supported the project and provided positive feedback, a successful tool was developed. This demonstrated the tool’s effectiveness.

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Comparative Analysis of Machine Learning Techniques with Principal Component Analysis on Kidney and Heart Disease

10.1109/icesc51422.2021.9533011 ◽

2021 ◽

Author(s):

Reena Chandra ◽

Manoj Kapil ◽

Avinash Sharma

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Heart Disease ◽

Comparative Analysis ◽

Principal Component ◽

Component Analysis ◽

Machine Learning Techniques ◽

Learning Techniques

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Heart Disease Prediction using Machine Learning Techniques

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst2183218 ◽

2021 ◽

pp. 42-47

Author(s):

Ramesh Ponnala ◽

K. Sai Sowjanya

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Linear Model ◽

Machine Learning Techniques ◽

Disease Prediction ◽

Huge Amount ◽

Healthcare Enterprise ◽

Learning Techniques ◽

Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

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Heart Disease Prediction using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9780.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 700-704

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Nearest Neighbour ◽

Decision Tree Classifier ◽

Support Vector Classifier ◽

Learning Techniques ◽

Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

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