A cardiovascular disease (CVD) is a disorder that affects the heart or blood vessels. Cardiovascular illnesses include heart attacks, heart failure, arrhythmia, coronary heart disease, and many others. Smoking, blood pressure, cholesterol level, diabetes, and being overweight all increase the risk of various cardiovascular illnesses. Age, gender, nutrition, and alcohol consumption can all be important risk factors for cardiovascular disease. Cardiovascular disorders are the leading cause of mortality all over the world. According to the 2019 study, almost 18 million individuals died from Cardiovascular illnesses, with heart attacks and stroke accounting for 85 percent of these deaths. In India, the projected number of CVD deaths in 1990 was 2.26 million, and the number of fatalities is expected to rise to 4.77 million by 2020. CVD increased from 1.6 percent to 7.4 percent in the rural region and from 1% to 13.2 percent in the urban area. Many researchers have worked on stroke prediction in recent years, employing various machine learning techniques such as Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), Xgboost (XGB), Naive Bayes, and others. In this paper, we describe a model that combines data preprocessing and scaling approaches to clean the dataset, different classifiers applied to this dataset, and obtained the highest accuracy of 88.52 percent and Roc AUC of 88.74 percent by applying Random forest over the test data.

One can find out more about the machine learning algorithms used to predict the likeliness of cardiovascular disease in the https://github.com/shubham20315/Prediction-of-likeliness-of-cardiovascular-disease-using-machine-learning-algorithms/blob/main/Heart%20Stroke%20Prediction.pdf and you can find out our working model in a mobile application at https://github.com/shubham20315/Prediction-of-likeliness-of-cardiovascular-disease-using-machine-learning-algorithms/blob/main/Heart-Stroke-Prediction-app.apk

We worked on the heart disease dataset, which was received from the UCI (University of California at Irvine) repository; this data set has 14 attributes with 303 instances, including age, gender, cp, trestbps, cho, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, and target. We worked on 10 attributes. We remove less impactful attributes like exang, oldpeak, slope and thal. The target field describes heart disease in the patient. The patient's heart problem is described in the target field. This is an integer value, with 0 indicating no heart disease and 1 indicating heart disease.

One needs Python 3.6 with standard libraries such as pandas, matplotlib, seaborn, numpy,sklearn and imblearn.

1. Download the code using https://github.com/shubham20315/Prediction-of-likeliness-of-cardiovascular-disease-using-machine-learning-algorithms/blob/main/Final_Code_project.ipynb
2. Downlaod the dataset using https://github.com/shubham20315/Prediction-of-likeliness-of-cardiovascular-disease-using-machine-learning-algorithms/blob/main/heart.csv
3. You can run the code in your local machine having python 3.6 and libraries or you can use the Google Collab for running code without any difficulty.