Platelets and Hematocrit in the Survival Model of Dengue Hemorrhagic Fever (DHF) Sufferers in Palopo

Fa’rifah, Riska Yanu; Poerwanto, Bobby

doi:10.4028/www.scientific.net/msf.967.3

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The cox regression model is a semiparametric regression model and fulfills the proportional hazard assumption, so it is commonly known as the cox proportional hazard regression model ( (Dukalang, 2019); (Fa'rifah and Poerwanto, 2019)). This method can estimate the hazard ratio used to compare hazard values in a group of individuals with other groups of individuals to fulfill the proportional hazard assumption.…”

Section: A Introductionmentioning

confidence: 99%

Survival Analysis with Cox Proportional Hazard Model for Tuberculosis (TBC) Patients

Nisa,

Poerwanto,

2023

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Survival analysis is a method in statistics which aims to analyze the relationship between time from thebeginning of observation until the occurrence of an event (response variable) with factors that have aninfluence on the event (predictor variables). To determine the relationship between the response variableand the predictor variable, where the response variable is the time until the event occurs, one method thatcan be used is the cox proportional hazard regression method. The data used in this research is data onhospitalizations of tuberculosis sufferers at Haji Makassar Hospital in 2022 because it has characteristicsthat are in accordance with the aim of survival analysis, namely to determine the relationship betweenthe life span of TBC patients and the factors that influence TBC disease. The results of the analysisobtained factors that significantly influence the recovery rate of patients with TBC are shortness ofbreath and smoking habits. The shortness of breath variable has an influence on the recovery rateof TBC patients, namely 0,3506, which means that TBC patients who do not experiencing shortnessof breath has a recovery rate of 0.3506 times the likelihood of recovery compared to patients whoexperience shortness of breath. Variable smoking habit was 0.7367, which means that patients withTBC did not smoking habit has a recovery rate of 0.7367 times recovered compared to patients who hada smoking habit

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Section: A Introductionmentioning

confidence: 99%

Survival Analysis with Cox Proportional Hazard Model for Tuberculosis (TBC) Patients

Nisa,

Poerwanto,

2023

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Evaluating the K-Means Analysis in Clustering Area Based on Estates Productivity in Tana Luwu Using Silhouette Index

Poerwanto

2021

J. Phys.: Conf. Ser.

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Tana Luwu is an area in South Sulawesi Province that consists of 4 districts or city, namely Luwu, East Luwu, North Luwu, and Palopo. Those regions have abundant natural resources, one of which is plantation crops. In this manuscript, the first objective was to group the 45 sub-districts in those districts into 2 to 5 clusters based on their productivity on providing plantation crops using K-Means Algorithm. Variables used in this study are coconut, palm, coffee, pepper, cocoa, cloves, and land area. Secondly, after the clusters were formed, those were evaluated using Silhouette Index to find out the most optimum number of clusters. As the result, it was found that the optimum number was two with Silhouette Index 0,8068 while the value of 3 clusters is 0,6468. For the values of 4 clusters and 5 clusters are 0,5029 and 0,5509 respectively.

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SEIAS-SEI model on asymptomatic and super infection malaria with imperfect vaccination

Maryam¹,

Abdy²,

Ali³

et al. 2021

J. Phys.: Conf. Ser.

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The aims of this research are to build a model of SEIAS-SEI on the spread of malaria; analysing the model; and conducting simulations to predict the number of cases of malaria in disease-free and endemic cases. This research is a theoretical study. This model uses suspected, exposed, infected and acute compartments. Analysis of the model using the matrix generation method, the data used in the simulation is an assumption data to cover the disease-free and endemic malaria cases and the model simulation using Maple. The results obtained are models of SEIRS-SEI in malaria deployments; model analysis generates equilibrium points and basic reproductive numbers to determine the status of malaria deployments in a region. Simulated results provide a sense of the difference between disease-free State and endemic is that there is a decrease in the number of infected and asymptomatic individual groups. In endemic cases, a decrease in the number of infected and asymptomatic individual groups in malaria spread takes a long time. In contrast to disease-free cases, a decline in the number of infected and asymptomatic individuals may take a short time.

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Platelets and Hematocrit in the Survival Model of Dengue Hemorrhagic Fever (DHF) Sufferers in Palopo

Cited by 3 publications

References 16 publications

Survival Analysis with Cox Proportional Hazard Model for Tuberculosis (TBC) Patients

Survival Analysis with Cox Proportional Hazard Model for Tuberculosis (TBC) Patients

Evaluating the K-Means Analysis in Clustering Area Based on Estates Productivity in Tana Luwu Using Silhouette Index

SEIAS-SEI model on asymptomatic and super infection malaria with imperfect vaccination

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