Cardiovascular Disease Prediction using Classification Algorithms of Machine Learning
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Cardiovascular disease is a major health burden worldwide in the 21st century. Human services consumptions are overpowering national and corporate spending plans because of asymptomatic infections including cardiovascular ailments. Consequently, there is an urgent requirement for early location and ...learn more
Project status: Published/In Market
Intel Technologies
Intel Python,
Other
Overview / Usage
Cardiovascular disease is a major health burden worldwide in the 21st century. Human services consumptions are overpowering national and corporate spending plans because of asymptomatic infections including cardiovascular ailments. Consequently, there is an urgent requirement for early location and treatment of such ailments. The information which is gathered by data analysis of hospitals is utilizing by applying different blends of calculations and algorithms for the early-stage prediction of Cardiovascular ailments. Machine Learning is one of the slanting innovations utilized in numerous circles far and wide including the medicinal services application for predicting illnesses. In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analysis of heart diseases and predicting the overall risks. The proposed experiment is based on a combination of standard machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), support vector machine (SVM) and Decision Tree. Most of the entities in this world are related in one way or another, at times finding a relationship between entities can help you make valuable decisions. Likewise, I will attempt to utilize this information as a model that predicts the patient whether they are having a Cardiovascular disease or on the other hand not. Moreover, the data analysis is carried out in Python using Jupyter Lab in order to validate the accuracy of all the Algorithm.
Methodology / Approach
Workflow of building Machine Learning Model Figure 1 indicates the steps followed in order to build the model in machine learning. Figure 1: Workflow of building Machine Learning Model Paper ID: SR20501193934 DOI: 10.21275/SR20501193934 3.2 Data Acquisition The dataset is collected from the Kaggle website. 3.3 Data Pre-Processing In order to build up a more accurate Machine Learning model, data preprocessing is required. Data pre-processing is the process of cleaning the data. It will remove all the NAN values from our data. This process is also known as Data Wrangling. This includes the identification of missing data, noisy data, and inconsistent data. 3.4 Proposed System Figure 2: Proposed System 3.5 Select Machine Learning Model Then the pre-processed data are identified using machine learning algorithms. We will be using the Classification Algorithm to compare the best accuracy from all. a) Input Variables of the study The data set consists of 14 IVS. The Machine Learning model is based on the identification of DV.
Technologies Used
Used Jupyter Notebooks
Programming Language- > Python
Libraries - > Pandas, Sklearn, Matplotlib, NumPy, SciPy