A Hybrid Machine Learning algorithm for Heart and Liver Disease Prediction Using Modified Particle Swarm Optimization with Support Vector Machine

Machine learning is now extensively applied in a variety of fields. Machine learning is employed like an efficient assistance mechanism in clinical diagnostics since vast amounts of data are readily available. Owing to heavy alcohol consumption, inhalation of contaminated gas, narcotics, food contamination, unhealthy life style people suffering from heart and liver disease has been significantly growing. Both heart and liver disease cause high mortality rate worldwide. It is critical to discover these diseases at an early stage in order to save people's lives. Incorporating machine learning classification algorithms into health-care organizations yields remarkable outcomes, allowing health-care practitioners to diagnose diseases more quickly and accurately. Machine learning techniques and tools aid in the extraction of useful information from datasets, resulting in more exact findings. In this study for heart and liver data classification, a hybrid model is created by combining support vector machine (SVM) approach and modified particle swarm optimization model. The data sets are collected from UCI machine learning repository. The results are calculated based on classification accuracy, error, correctness, recall as well as F1 score. The results obtained is compared with SVM, hybrid particle swarm optimization support vector machine algorithm (PSOSVM), hybrid Crazy particle swarm optimization support vector machine algorithm (CPSOSVM).