Abstract
Both heart disease and cancer are the major causes of death in the world. As diagnosing it early helps us from the risk of it occurring, but the high costs are a major hurdle. Feature selection is one of the important techniques that can be used to improve the classification process and to reduce the cost of diagnosis by relying on a specific set of features instead of using all features, in addition to identifying those features that play the largest role in the classification improvement process for each of cervical cancer and heart disease by using heart failure clinical records and risk factors of Cervical Cancer datasets. Five machine learning algorithms were used for classification and then the Crow Search Algorithm (CSA) was used for feature selection to improve the performance of the model. SVM act as a good classification algorithm to predict both heart disease and cervical cancer. The proposed method shows 75% accuracy for cardiac patients and 97% accuracy for cervical cancer patients.
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Aloss, A., Sahu, B., Deeb, H., Mishra, D. (2022). A Crow Search Algorithm-Based Machine Learning Model for Heart Disease and Cervical Cancer Diagnosis. In: Mallick, P.K., Bhoi, A.K., González-Briones, A., Pattnaik, P.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 860. Springer, Singapore. https://doi.org/10.1007/978-981-16-9488-2_27
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DOI: https://doi.org/10.1007/978-981-16-9488-2_27
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