Predicting the Mortality of Patients with Leukemia Using Artificial Intelligence

Mahdavi, Maryam; Ariaiinezhad, Shiva; Kordestani, Parastou; Heidarizadeh, Khadijeh; Eskandarzadeh, Ahmad; Mohammadi, Rasool

doi:10.22087/ijac.2021.207425

Predicting the Mortality of Patients with Leukemia Using Artificial Intelligence

Document Type : Research

Authors

Maryam Mahdavi ¹

Shiva Ariaiinezhad ¹

Parastou Kordestani ²

Khadijeh Heidarizadeh ²

Ahmad Eskandarzadeh ³

Rasool Mohammadi ⁴

¹ Student Research Committee, School of Nursing and Midwifery, Lorestan University of Medical Sciences, Khorramabad, Iran

² Critical care and Emergency Nursing, Faculty of Nursing & Midwifery, Lorestan University of Medical Sciences, Khorramabad, Iran

³ Stu.Res

⁴ Nutritional Health Research Center, School of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran

10.22087/ijac.2021.207425

Abstract

Several factors must be considered when predicting mortality in patients with hematological malignancies. These factors and characteristics complicate the ability of doctors’ and nurses to predict the prognosis of these diseases. This study aimed to develop a mortality prediction model for leukemia patients using artificial intelligence and a nearest-neighbor genetic algorithm. This retrospective study used the medical records of 235 patients with leukemia at the Ahvaz Oncology Center from 2016 to 2019. To provide a mortality prediction model, a genetic algorithm and nearest neighbor were used. A genetic approach was employed to identify the determinants of mortality, and the nearest-neighbor technique was utilized to enhance model accuracy. Ultimately, the diagnostic power of the mortality prediction model was assessed using accuracy, sensitivity, and specificity criteria. The laboratory values and variables incorporated into the genetic algorithm revealed that mechanical ventilation, hemodialysis, neutropenia, and bone marrow transplantation significantly influenced the mortality rate of patients with leukemia. The diagnostic accuracy of the genetic algorithm introduced in this study was 77.4%, with a sensitivity of 78.2% and specificity of 82%. The results showed the artificial intelligence algorithm in predicting mortality in leukemia patients.

Keywords

Artificial intelligence

Leukemia

Genetic Algorithm

K-nearest neighbor algorithm

Oncology

Intensive Care Unit

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