Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format.
Natural Language processing has been one of the challenging field of computational linguistics. Language processing occurs in several steps in which Named Entity Recognition is one of the prominent phase. NER frameworks have been contemplated and created generally for a considerable length of time, however precise frameworks utilizing profound neural systems (NN) have just been presented over the most recent couple of years. We present a far reaching review of profound neural system models for NER, and balance them with past ways to deal with NER dependent on highlight designing and other regulated or semi-administered learning calculations.
The class imbalance problem presents an important challenge to the data mining community, in which the number of examples of one class is more than the others. This problem is characterized by a different distribution of cases between all the classes. In this paper, our goal is to study the various challenges of class imbalance problem and provide a comparative study of the current development of research in learning from imbalanced data. We provide a thorough understanding of the nature of the problem, the methods used for data balancing, the learning objectives and assessment metrics used for getting measurable performance, the stated research solutions and the imbalanced problem in multiple classes. This paper highlights the significant opportunities and challenges in the field and provides potential future research directions in the class imbalance problem.
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