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Journal : PELS (Procedia of Engineering and Life Science)

Development of a Machine Learning-Based Web Application for Quality Justification in Dialysis Healthcare Nisak, Umi Khoirun; Kautsar, Irwan Alnarus; Ilmi, Laili Rahmatyul; Natasya, Nabila Insyira; Cholifah, Cholifah; Aditiawardana, Aditiawardana
Procedia of Engineering and Life Science Vol. 6 (2024): The 3rd International Scientific Meeting on Health Information Management (3rd ISMoHI
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v6i0.1962

Abstract

The quality of healthcare services, especially for chronic conditions like kidney failure requiring dialysis, is critical. This study aims to develop a machine learning-based web application to evaluate and justify dialysis healthcare quality. Conducted at Siti Khodijah Hospital from January to June 2024, the research employed a developmental and experimental design involving 123 medical professionals. The methodology included needs assessment, system design, algorithm selection, data collection, model training, system integration, and validation. The web application, named Renal Data Processor, features user-friendly navigation, robust data visualization, and machine learning algorithms. It provides real-time analysis and predictive insights, allowing healthcare providers to make data-driven decisions. Results showed significant improvements, including a 20% reduction in data entry time and a 15% enhancement in nurse certification tracking efficiency. User feedback indicated high satisfaction with the application's functionality and its impact on workflow efficiency. In conclusion, the Renal Data Processor has enhanced dialysis healthcare quality by streamlining data management and providing actionable insights. This study demonstrates the potential of machine learning to transform healthcare delivery and outcomes, suggesting further research to expand its capabilities and applicability in other healthcare settings.
Dengue Hemorrhagic Fever in Jepara Indonesia: Reporting Data From The Discrict Disease Surveillance Wijayanti, Nani Dwi; Ilmi, Laili Rahmatul; Nisak, Umi Khoirun; Widayati, Eka; Pramesti, Febryan Nidya; Ningsih, Eka Rahma
Procedia of Engineering and Life Science Vol. 6 (2024): The 3rd International Scientific Meeting on Health Information Management (3rd ISMoHI
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v6i0.1969

Abstract

Indonesia is a tropical country vulnerable to infectious diseases, including dengue fever caused by mosquito bites. Dengue fever cases are a burden on the world because they can cause death. WHO reported that in May 2024, there were a total of 465 cases in districts/cities in 34 provinces, the national death rate until May 2024 was 777 deaths. Recording a complete medical record can support good disease reporting. This study aims to determine the distribution of dengue fever cases in Jepara district, Central Java in 2023-2024. The method of this study is descriptive. The population is disease reports in 2023 and 2024, and the sample used is the dengue fever disease report for the period January - May 2024 with random sampling techniques. Data imported from Microsoft Access is processed with a stata application and narrated descriptivelyThe number of dengue fever sufferers spread across 16 sub-districts is 3,549 with 1,728 male and 1,821 female sufferers. Of the 16 sub-districts, the highest number is in Pecangaan District 543 cases, and the lowest cases are in Karimunjawa District. The category of patients based on the highest age group in category 1 (0-4 years old) was 2,136 (60.17%), with the highest mortality rate in category 2 (5-14 years old) as many as 12 people and the highest CFR at Kartini Hospital with a total of 19%, namely 12.9% in male patients and 7.24% in female patients. (dengue fever, cases, reporting). Dengue fever, Jepara District, Case Fatality Rate, Age group, Epidemiology