Chronic Renal Disease Prediction using Clinical Data and Different Machine Learning Techniques

Md Mohsin Sarker Raihan, Eshtiak Ahmed, Asif Karim, Sami Azam, M. Raihan, Laboni Akter, Md Mehedi Hassan

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

5 Lataukset (Pure)


Chronic Renal Disease (CRD) or Chronic Kidney Disease (CKD) is defined as the continuous loss of kidney function. It's a long-term condition in which the kidney or renal doesn't work properly, gets damaged and can't filter blood on a regular basis. Diabetes, high blood pressure, swollen feet, ankles or hands and other disorders can cause chronic renal disease. By gradual progression and lack of treatment, it can lead to kidney failure. A prior prognosis of CKD can nourish the quality of life to a higher range in such circumstances and can enhance the attribute of life to a larger province. Now a days, bioscience is playing a significant role in the aspect of diagnosing and detecting numerous health conditions. Machine Learning (ML) as well as Data Mining (DM) methods are playing the leading role in the realm of biosciences. Our objective is to predict and diagnose (CKD) with some machine learning algorithms. In this study, an attempt to diagnose chronic renal disease has been taken with four ML algorithms named XGBoost, Adaboost, Logistic Regression (LR) as well as Random Forest (RF). By using decision tree-based classifiers and analyzing the dataset with comparing their performance, we attempted to diagnose CKD in this study. The results of the model in this study showed prosperous indications of a better prognosis for the diagnosis of kidney diseases. Considering and contemplating the performance analysis, it is accomplished that Random Forest ensemble learning algorithm provides better classification performance than other classification methods.

Otsikko2nd International Informatics and Software Engineering Conference, IISEC 2021
ToimittajatAsaf Varol, Ali Yazici, Cihan Varol, Meltem Eryilmaz
ISBN (elektroninen)9781665407595
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Informatics and Software Engineering Conference - Ankara, Turkki
Kesto: 16 jouluk. 202117 jouluk. 2021


Nimi2nd International Informatics and Software Engineering Conference, IISEC 2021


ConferenceInternational Informatics and Software Engineering Conference


  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence


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