An experiment-based investigation into machine learning for predicting coronary heart disease

Lamiaa Mohammed Salem  Akoosh; Farheen  Siddiqui; Sherin Zafar; Sameena Naaz; M Afshar  Alam

An experiment-based investigation into machine learning for predicting coronary heart disease

Lamiaa Mohammed Salem Akoosh, Farheen Siddiqui, Sherin Zafar, Sameena Naaz, M Afshar Alam

School of Arts, Humanities, and Social Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

Extensive inquiry has been conducted to explore potential applications of machine learning methodologies in the realm of cardiovascular disease management. To facilitate a more comprehensive investigation This study explores machine learning algorithms, specifically Support Vector Machines (SVM) and Artificial Neural Networks (ANN), for disease identification, focusing on cardiovascular diseases. Utilizing a Kaggle dataset of around seventy thousand medical records, the research aims to refine methodology and assess performance variations. SVM and ANN techniques are applied to the Kaggle dataset, revealing SVM accuracies of 0.9997 (default), 0.9998 (RBF kernel, C=100.0), and 1.000 (linear kernel, C=1000.0). The Feedforward neural network, using Adam optimization across 50 batches and 10 epochs, achieved perfect accuracy of 1.000.

Original language	English
Journal	Journal of Statistics and Management Systems
Volume	27
Issue number	2
Publication status	Published - 30 Mar 2024

Cite this

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title = "An experiment-based investigation into machine learning for predicting coronary heart disease",

abstract = "Extensive inquiry has been conducted to explore potential applications of machine learning methodologies in the realm of cardiovascular disease management. To facilitate a more comprehensive investigation This study explores machine learning algorithms, specifically Support Vector Machines (SVM) and Artificial Neural Networks (ANN), for disease identification, focusing on cardiovascular diseases. Utilizing a Kaggle dataset of around seventy thousand medical records, the research aims to refine methodology and assess performance variations. SVM and ANN techniques are applied to the Kaggle dataset, revealing SVM accuracies of 0.9997 (default), 0.9998 (RBF kernel, C=100.0), and 1.000 (linear kernel, C=1000.0). The Feedforward neural network, using Adam optimization across 50 batches and 10 epochs, achieved perfect accuracy of 1.000.",

author = "Akoosh, {Lamiaa Mohammed Salem} and Farheen Siddiqui and Sherin Zafar and Sameena Naaz and Alam, {M Afshar}",

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AU - Akoosh, Lamiaa Mohammed Salem

AU - Siddiqui, Farheen

AU - Zafar, Sherin

AU - Naaz, Sameena

AU - Alam, M Afshar

PY - 2024/3/30

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AB - Extensive inquiry has been conducted to explore potential applications of machine learning methodologies in the realm of cardiovascular disease management. To facilitate a more comprehensive investigation This study explores machine learning algorithms, specifically Support Vector Machines (SVM) and Artificial Neural Networks (ANN), for disease identification, focusing on cardiovascular diseases. Utilizing a Kaggle dataset of around seventy thousand medical records, the research aims to refine methodology and assess performance variations. SVM and ANN techniques are applied to the Kaggle dataset, revealing SVM accuracies of 0.9997 (default), 0.9998 (RBF kernel, C=100.0), and 1.000 (linear kernel, C=1000.0). The Feedforward neural network, using Adam optimization across 50 batches and 10 epochs, achieved perfect accuracy of 1.000.

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JO - Journal of Statistics and Management Systems

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