Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease.

Journal: Frontiers in artificial intelligence
Published Date: Jan 5, 2024

Abstract

In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.

Authors

  • Nitasha Khan
    Department of Electrical Engineering, Nazeer Hussain University, Karachi, Pakistan.
  • Muhammad Amir Raza
    Department of Electrical Engineering, Mehran University of Engineering and Technology, Khairpur Mirs, Sindh, Pakistan.
  • Nayyar Hussain Mirjat
    Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan.
  • Neelam Balouch
    Department of Zoology, Shah Abdul Latif University Khairpur Mirs, Khairpur Mirs, Pakistan.
  • Ghulam Abbas
    School of Electrical Engineering, Southeast University, Nanjing, China.
  • Amr Yousef
    Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia.
  • Ezzeddine Touti
    Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia.

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