A Comparative Analysis of K-Nearest Neighbors and Naive Bayes Algorithms for Classifying Abnormal and Normal Spine Datasets
Abstract
The accurate classification of spine datasets into "Abnormal" and "Normal" classes is crucial for early diagnosis and effective treatment planning in orthopedic medicine. In this paper, we present a comparative study of two popular machine learning algorithms, K-Nearest Neighbors (KNN) and Naive Bayes, applied to a spine dataset. The objective is to determine which algorithm performs better in terms of accuracy and suitability for this specific classification task. We evaluate both methods using a dataset consisting of 310 spine samples, labeled as either "Abnormal" or "Normal." Our results demonstrate that Naive Bayes outperforms KNN, achieving an accuracy of 89%, compared to KNN's accuracy of 87%. We also discuss the implications of these findings and highlight potential areas for further research in spine dataset classification.
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Introduction
AI is a subfield of computerized reasoning that spotlights on the improvement of calculations and models that empower PCs to learn and pursue expectations or choices without being expressly customized. It includes the investigation of measurable and computational strategies that permit machines to gain examples and concentrate experiences from information [1].
Exact expectation assumes a crucial part in different applications, like clinical finding, monetary estimating, and client conduct examination. Multi-facet Perceptron and Strategic Relapse are broadly utilized calculations in the field of prescient demonstrating [2][3]. This paper plans to direct a thorough report to look at their exhibition and dissect their materialness for various expectation errands. The review explores various assessment measurements to evaluate the forecast exactness and viability of the two calculations on vote dataset.
Conclusion
In conclusion, our study demonstrates that Naive Bayes is a promising approach for the classification of spine datasets, outperforming the K-Nearest Neighbors algorithm in terms of accuracy. However, the choice of the most appropriate algorithm should consider specific application requirements and performance metrics like precision and recall. The findings of this study could contribute to the development of more accurate and reliable models for spine dataset classification, ultimately enhancing medical decision-making and patient care.