Analisis Ketahanan Hidup Pasien Kanker Serviks di Rumah Sakit Islam Jemursari Surabaya Menggunakan Regresi Cox - Regularisasi

Insani, Reka Manika (2023) Analisis Ketahanan Hidup Pasien Kanker Serviks di Rumah Sakit Islam Jemursari Surabaya Menggunakan Regresi Cox - Regularisasi. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kanker serviks atau kanker leher rahim merupakan tumor ganas yang mengancam kesehatan wanita karena infeksi virus HPV (Human Papilloma Virus). Di Indonesia, prevalensi kanker serviks merupakan yang tertinggi di antara semua jenis kanker ginekologi. Salah satu tolak ukur standar keberhasilan pengobatan kanker serviks yaitu ketahanan hidup pasien kanker serviks selama lima tahun. Oleh karena itu, dalam studi ini dilakukan penelitian terhadap ketahanan hidup penderita kanker serviks dengan sampel yang dikumpulkan dari Rumah Sakit Islam Jemursari Surabaya (RSI Jemursari) selama periode penelitian 2018-2022. Tantangan utama dalam analisis penelitian di RSI Jemursari adalah keterbatasan data akibat mayoritas pasien hanya melakukan diagnosis awal dan memilih rujuk ke rumah sakit lainnya. Ketersediaan data hanya terdapat 4 pasien yang mengalami event atau meninggal. Hal tersebut berpengaruh terhadap model yang dihasilkan dimana model tersebut tidak layak. Dengan keterbatasan tersebut, untuk mengetahui ketahanan hidup penderita kanker serviks diperlukan analisis secara khusus disesuaikan dengan menggunakan regresi Cox - regularisasi. Faktor-faktor yang berpotensi dalam model di antaranya faktor sosio demografi, riwayat medis, serta patologi dan klinis. Karakteristik pasien kanker serviks di RSI Jemursari mayoritas terdiagnosis pada stadium I dan memiliki median waktu ketahanan hidup hingga mengalami event yaitu 23 hari. Hasil pemodelan menggunakan Cox - regularisasi diperoleh variabel yang berpengaruh terhadap ketahanan hidup pasien kanker serviks yaitu stadium. Nilai hazard ratio pasien kanker serviks yang terdiagnosis pada stadium IV memiliki risiko 2,307384kali lebih tinggi untuk meninggal daripada pasien dengan diagnosis pada stadium I.
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Cervical cancer is a malignant tumor that threatens women's health due to the infection of Human Papillomavirus (HPV). In Indonesia, the prevalence of cervical cancer is the highest among all types of gynecological cancers. One of the standard benchmarks for the success of cervical cancer treatment is the five-year survival rate of cervical cancer patients. Therefore, this study researched the survival rate of cervical cancer patients with a sample collected from Islamic Hospital Jemursari Surabaya (RSI Jemursari) during the period of research from 2018 to 2022. The main challenge in the research analysis at RSI Jemursari is the limited data due to the majority of patients only receiving initial diagnosis and choosing to be referred to other hospitals. Only four patients with an event (death) were available in the data. This significantly affects the resulting model, making it inadequate. With such limitations, to determine the survival rate of cervical cancer patients, a specially adjusted analysis using Cox-regression with regularization is needed. Potential factors in the model include socio-demographic factors, medical history, as well as pathology and clinical data. The majority of cervical cancer patients at RSI Jemursari were diagnosed at stage I and had a median survival time until the event of 23 days. The modeling results using Cox-regression with regularization revealed that the influential variable on the survival rate of cervical cancer patients is the stage of cancer. The hazard ratio value for cervical cancer patients diagnosed at stage IV is 2,307384 times higher in the risk of death compared to patients diagnosed at stage I.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Cervical Cancer, Cox Regression, Survival analysis, Analisis Survival, Kanker Serviks, Regresi Cox
Subjects:	R Medicine > R Medicine (General) > R853.S7 Survival analysis (Biometry) R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Reka Manika Insani
Date Deposited:	15 Sep 2023 07:45
Last Modified:	15 Sep 2023 07:45
URI:	http://repository.its.ac.id/id/eprint/104593

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