Data Mining Genome-Based Algorithm for Optimal Gene Selection and Prediction of Colorectal Carcinoma

Banjoko, Alabi Waheed

doi:10.5336/biostatic.2020-77341

Cited by 1 publication

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“…Automating the diagnostic process in health care services by using data-mining techniques is fast gaining recognition ( 9 ). Computerised classification of binary response data, such as the heart disease data used in this study, using data-mining algorithms can be useful for medical personnel to quickly diagnose their patients at a low cost ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test

Banjoko¹,

Abdulazeez²

2021

MJMS

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Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.

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Section: Introductionmentioning

confidence: 99%