Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

🤖 Plain-English Summary

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score.

🔑 Key Findings

• In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients.
• However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis.
• This inconsistency led to a higher probability of misprediction and a misled result.

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