Abstract:Abstract: Heart disease is one of the leading causes of death worldwide, making early detection and accurate diagnosis crucial for reducing mortality rates and improving patient outcomes. This study aims to evaluate the…
effectiveness of four machine learning algorithms—Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—in predicting heart disease, with a focus on enhancing model performance using Linear Discriminant Analysis (LDA) for feature reduction. Among the models, SVM achieved the highest accuracy at 84.24%, followed by Logistic Regression at 83.70%. Although Random Forest and KNN showed lower accuracies, all models benefited from LDA's dimensionality reduction. This study suggests that SVM, combined with LDA, offers an optimal solution for early and accurate heart disease prediction in the healthcare industry.
Keywords: feature reduction; heart disease; linear discriminant analysis (LDA); machine learning; SVM
Abstrak: Penyakit jantung merupakan salah satu penyebab utama kematian di seluruh dunia, sehingga deteksi dini dan diagnosis yang akurat sangat penting untuk menurunkan angka kematian dan meningkatkan hasil pengobatan pasien. Penelitian ini bertujuan untuk mengevaluasi efektivitas empat algoritma pembelajaran mesin—Regresi Logistik, Random Forest, Support Vector Machine (SVM), dan K-Nearest Neighbors (KNN)—dalam memprediksi penyakit jantung, dengan fokus pada peningkatan kinerja model menggunakan Analisis Diskriminan Linear (LDA) untuk reduksi fitur. Di antara model yang diuji, SVM mencapai akurasi tertinggi sebesar 84,24%, diikuti oleh Regresi Logistik dengan 83,70%. Meskipun Random Forest dan KNN menunjukkan akurasi yang lebih rendah, semua model memperoleh manfaat dari reduksi dimensi yang diberikan oleh LDA. Studi ini menunjukkan bahwa SVM yang dikombinasikan dengan LDA merupakan solusi optimal untuk prediksi penyakit jantung secara dini dan akurat dalam industri kesehatan.
Kata kunci: linear discriminant analysis (LDA); machine learning; penyakit jantung; reduksi fitur; SVM.
Abstract:The Family Planning program has long been known as an important intervention in efforts to reduce maternal and child mortality rates. This research aims to analyze the effectiveness of the Family Planning Program in reducing…
cing maternal and child mortality rates using the Systematic Literature Review (SLR) method. This research uses the SLR method by collecting and analyzing literature from various academic databases. Inclusion criteria included studies evaluating the impact of Family Planning programs on maternal and child mortality rates, published between 2019 and 2024. The literature selection process was carried out in two stages: initial screening and quality assessment using PRISMA guidelines. A total of 15 studies met the inclusion criteria and were analyzed in this study. The findings show that the Family Planning Program is effective in reducing maternal mortality through increasing access to reproductive health services, health education, and use of contraception. Apart from that, the Family Planning program is also effective in reducing child mortality by improving maternal health and preventing unwanted pregnancies. Challenges identified include limited access to health services in remote areas, cultural barriers, and lack of policy support. Family Planning programs have proven effective in reducing maternal and child mortality rates. To increase its effectiveness, it is necessary to increase accessibility, service quality, and adequate policy support. It is hoped that these recommendations will help in improving the implementation of Family Planning programs and maternal and child health policies in the future.
Abstract:Indonesia is one of the countries affected by the COVID-19 pandemic. The elderly are the group most at risk for morbidity and mortality due to COVID-19. West Aceh Regency is one of the areas with confirmed cases of COVID-19.…
-19. This study aims to analyze the behavior of preventing COVID-19 in the elderly group. This type of research is descriptive analytic, with a quantitative approach. The sampling technique used was total sampling. Data collection techniques using a questionnaire instrument. Data analysis in this study used three analyzes, namely univariate, bivariate, and multivariate. The results showed that 69.8% of the elderly with less Perceived Susceptibility but had good COVID-19 prevention. 64.7% of the elderly with good Perceived Severity but have less prevention. 63.6% of respondents have good Perceived Benefits as well as COVID-19 prevention. 69.1% of respondents have less Perceived Barriers but have good COVID-19 prevention and 69.1% have good Cues to action plus good COVID-19 Prevention. The most dominant factors that can be used in COVID-19 prevention are Perceived Severity with an OR value of 2.77 and Perceived Barriers with an OR value of 2.76. The Health Belief Model has a relationship and can cause behavioral changes in preventing the potential for COVID-19 in the elderly.
Abstract:The maternal mortality rate in Indonesia remains a serious issue, primarily due to the lack of quality childbirth services. One approach applied is the loving care for mothers, which is a service approach that respects the…
he rights, comfort, and wishes of mothers during the childbirth process. Research objective: To understand the attitudes of midwives regarding loving care for mothers during childbirth. Research method: This type of research is descriptive. The research locations are Lidya Clinic, Katarina Clinic, Kasih Bunda Clinic, Pratama Bertha Clinic, Pratama Romauli Clinic. Research results: The results indicate that 21 respondents (70%) have a positive attitude, while 9 respondents (30%) have a negative attitude. Conclusion: Based on the research results, it shows that midwives have a positive attitude to awards maternal affection care during labor. The researchers suggest that midwives at Collaborative Clinics of Santa Elisabeth Health Sciences College Medan should maintain their positive attitude in providing services