Cardiovascular Disease Prediction Using Randelistic Algorithm

Diagnosis of heart diseases pose a major problem due to human error and faulty interpretation of data. Existing medical tools are costly and has a higher percentage of true negatives, and do not fare well in the areas of prediction., putting people at danger due to carelessness. Timely prediction plays an important role in saving patient's lives. Prediction models like these which use Machine Learning can be used in Hospitals and as well as for personal use to reduce the number of cases of True Negative due to diagnostic negligence and to keep track of various symptoms over a period of time that acts as a detailed history to improve accuracy of the learning model designed using various mathematically intensive algorithms. The solution proposed is to use multiple algorithms namely Logistic Regression[1], Random Forest Algorithm[1] and Decision trees[1] in the Boosting process, so as to reduce the number true negatives and false positives and get the best result during the majority voting.