Abstract
Cardiovascular Diseases (CVDs) are the primary cause for the sudden death in the world today from the past few years the disease has emerged greatly as a most unpredictable problem, not only in India the whole planet facing the criticality. So, there is a desperate need of valid, accurate and practical solution or application to diagnose the CVD problems in time for mandatory treatment. Predicting the CVD is a great challenge in the health care domain of clinical data analysis. Machine learning Algorithms (MLA) and Techniques has been vastly developed and proven to be effective and efficient in predicting the problems using the past data. Using these MLA techniques and taking the clinical dataset which provided by the healthcare industry. Different studies were takes place and tried only a small part into predicting CVD with ML Algorithms. In this thesis, we propose the different novel methodology which concentrates at finding appropriate features by using MLA techniques resulting at finding out the accurate model to predict CVD. In this prediction model we are trying to implement the models with different combinations of features and several known classification techniques such as Deep Learning, Random Forest, Generalised Linear Model, Naïve Bayes, Logistic Regression, Decision Tree, Gradient Boosted trees, Support Vector Machine, Vote and HRFLM and we have got an higher accuracy level and of 75.8%, 85.1%, 82.9%, 87.4%, 85%, 86.1%, 78.3%, 86.1%, 87.41%, and 88.4% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).
Highlights
It is hard to recognize Cardio vascular Diseases (CVD) because of a couple of contributory risk factors like raised cholesterol levels, diabetes, hypertension, dropping heart rate etc., like different problems
The most elevated accuracy is accomplished by the proposed classification method (HRFLM) algorithmic model is compared with other algorithms
Recognizing the preparation of raw medical datasets of CVD will be useful in the future to save the life of human and early detection of cardio vascular problems
Summary
It is hard to recognize Cardio vascular Diseases (CVD) because of a couple of contributory risk factors like raised cholesterol levels, diabetes, hypertension, dropping heart rate etc., like different problems. Various methods like Neural Networks(NN) and Data Mining(DM) techniques are used to get to know the intensity of CVD among different patients. The intensity of the problem is arranged depends on diverse strategies like Naive Bayes(NB), K-NN, Decision Tree(DT), and Genetic Algorithm(GA) [1]. The idea of CVD is unpredictable the contamination ought to be dealt with circumspectly. Not doing as such may impact the heart or cause abrupt passing. DM with arrangement has an immense impact in the conjecture of CVD and information assessment. Different strategies have been utilized for information deliberation by utilizing realized information digging techniques for anticipating CVD.
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