With the frequent decline in people’s health due to the hectic lifestyle, increased levels of workload and intake of fast food, there has been an unfortunate growth in the number of patients suffering from cardiovascular diseases each year. Around the world, millions of people die each year due to cardiovascular diseases. While the statistics are eye-opening, with the vast amount of data about heart patients in our hands, we can save millions by detecting the risk at an early stage. With the recent advances in soft computing and fuzzy logic, various algorithmic approaches are employed to tackle the issue of cardiovascular risk assessment through machine learning. Using some of the algorithms of machine learning like Logistic Regression (LR), Naïve Bayes (NB), Support vector machine (SVM), and Decision tree (DT), Random Forest (RF) and K-Nearest Neighbours (KNN) classifiers, a model can be built to predict the risk accurately. In this paper, we have analysed each of the above methods normally and through feature engineering techniques like transformation through Principal Component Axes and considering different train-test folds to find the best performing model, which was found to be KNN in terms of all metrics and Logistic Regression in terms of accuracy.

由于忙碌的生活方式，工作量的增加和快餐的摄入，人们的健康状况经常下降，因此，每年患有心血管疾病的患者数量在不断增加。全世界每年有数百万人死于心血管疾病。尽管统计数字令人大开眼界，但由于掌握了有关心脏病患者的大量数据，我们可以通过在早期发现风险来节省数百万美元。随着软计算和模糊逻辑的最新进展，采用了各种算法方法来通过机器学习解决心血管疾病风险评估的问题。使用某些机器学习算法，例如逻辑回归（LR），朴素贝叶斯（NB），支持向量机（SVM）和决策树（DT），通过随机森林（RF）和K最近邻（KNN）分类器，可以构建模型来准确预测风险。在本文中，我们通常通过特征工程技术（例如，通过主分量轴进行转换）并考虑了不同的训练测试折叠来找到最佳性能的模型，从而分析了上述每种方法，从而发现了性能最佳的模型，该模型在所有指标和Logistic回归的准确性。