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A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

Computer Systems Science & Engineering 2024年第5期48卷 1085-1112页

作者： Varada Rajkumar Kukkala Surapaneni Phani Praveen naga satya koti mani kumar tirumanadham Parvathaneni naga Srinivasu Department of Computer Science&Engineering(AIML) MLR Institute of TechnologyHyderabad500043India Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science&Engineering Sir C.R.Reddy College of EngineeringEluru534001India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular ***,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)*** findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning *** work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization *** studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

关键词： Grey wolf optimization ant colony optimization Z-Score interquartile range(IQR) AdaBoost outlier

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