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ASTHMA PREDICTION USING CLASSIFIER DATA FROM MACHINE LEARNING

G. GNANCY SUBHA1, D. PONJAYAVARTHINI2, G. TEJASRI, B. KALPANA4,

S. SHANMUGAPRIYA5

DEPARTMENT OF BIOMEDICAL ENGINEERING- J.N.N.I.E
1ASSISTANT PROFESSOR- DEPARTMENT OF BIOMEDICAL ENGINEERING, J.N.N.I.E

2,3,4 DEPARTMENT OF BIOMEDICAL ENGINEERING, J.N.N.I.E

ABSTRACT: Shortness of breath, wheezing, and frequently fatal attacks are symptoms of asthma, a lung condition brought on by airway blockage and inflammation. Asthma sufferers must be ready to anticipate severe exacerbations brought on by uncontrolled asthma. As a result, a framework that can precisely predict whether a patient will develop asthma is needed. In this study, an efficient mechanism for predicting asthma disease was devised by mining the data included in patients' prior health records. We make use of machine learning algorithms to illustrate the results. Four machine learning classification algorithms, KNN, ANN, SVM, and linear regression, were used in these trials to predict asthma disease at an early stage. Many measures are used to evaluate the effectiveness of all four models. The proportion of cases that are correctly and incorrectly classified serves as a gauge of a classification system's accuracy. The outcomes of the tests show that SVM approaches have the greatest accuracy. The experiment results are displayed using MATLAB software.

KEYWORDS:     Accuracy, KNN, ANN, SVM, Logistic Regression, MATLAB, Asthma Prediction, Machine Learning