Applying Machine Learning Techniques for Comparative Analysis of Various Diseases

doi:10.23940/ijpe.24.06.p5.379390

Abstract

Abstract: Artificial Intelligence (AI) and Machine Learning techniques (ML) play a vital and significant role in finding the solutions to real life problems and making our life smooth. When this machine learning is used for the medical world in prediction models it will become very helpful for the experts in making their diagnostic decisions quickly and more accurately. Implementation of these advanced techniques will lead medical science to an improved version of its. In this comparative study six different learning techniques i.e. Decision Tree (DT), Support -Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF) and eXtreme Gradient Boost(XGB) are analyzed on the basis of some parameters like Time complexity, f-score, recall, accuracy and precision. In the experimental model, these techniques are implemented on various diseases like Heart disease, Breast cancer and Obesity diagnosis. The overall dataset is divided into 80:20 ratios as training and testing datasets. The results obtained from the given setup show that the DT will give the optimized results for all diseases while the time complexity is optimized for KNN and XGB.

Key words: decision tree, KNN, XG boost, heart disease, obesity, breast cancer

Shikha Singh, Sumit Badotra, and Nitin Arvind Shelke. Applying Machine Learning Techniques for Comparative Analysis of Various Diseases [J]. Int J Performability Eng, 2024, 20(6): 379-390.

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