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With the rapid growth of technology and data, the healthcare domain has emerged as one of the most important research areas in the modern period. Machine Learning is a novel method for disease prediction and diagnosis. This study demonstrates how machine learning can be used to forecast disease based on symptoms. Techniques of Machine learning such as Bayes, Random Forest, and SVM are used to forecast the disease on the supplied dataset. The research determines which algorithm is the best based on its accuracy. The accuracy of an algorithm is determined by its performance on a particular dataset. One of the most significant disorders is heart disease. We discovered machine learning models to predict heart problems in order to lower the incidence of death caused by heart disease. In this paper, we used a dataset from 1988 that included four databases: Cleveland, Hungary, Switzerland, and Long Beach V., and applied an algorithms to it to obtain the results. Previous studies had lower accuracy, therefore we focused on this research to enhance accuracy rate, precision, and recall which are very crucial parameters in medical field, in order to forecast heart problems and rescue patients. In this paper, we worked on different algorithms such as SVM, Random Forest, Naïve Bayes, Neural Network and Decision Tree. The model was implemented using the Python programming language. Analysis result indicates that SVM and Decision Tree algorithms have achieved highest accuracy which is 98.05%.

In the medical field various motion tracking techniques like block matching, optical flow, and histogram of oriented optical flow (HOOF) are being experimented for the abnormality detection. The information furnished by the existing techniques is inadequate for medical diagnosis. This technique has an inherent drawback, as the entire image is considered for motion vector calculation, increasing the time complexity. Also, the motion vectors of unwanted objects are getting accounted during abnormality detection, leading to misidentification / misdiagnosis. In this research, our main objective is to focus more on the region of abnormality by avoiding the unwanted motion vectors from the rest of the portion of the heart, allowing better time complexity. Proposed a region-based HOOF (RHOOF) for blood motion tracking and estimation; after experimentation, it is observed that RHOOF is four times faster than HOOF. The performance of supervised machine learning techniques was evaluated based on accuracy, precision, sensitivity, specificity, and area under the curve. In the medical field more importance is given to the sensitivity than accuracy. Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work. An algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD).