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Logistic Regression with Hyper Parameter Tuning Optimization for Heart Failure Prediction Herwanto, Teguh; Kodri, Wan Ahmad Gazali; Aziz, Faruq; Hewiz, Alya Shafira; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.3

Abstract

Heart failure is a major public health concern that causes a substantial number of deaths worldwide. Risk factor analysis is required to diagnose and treat patients with heart failure. The logistic regression with hyper parameter tuning optimization is presented in this research, with ejection fraction, high blood pressure, age, and  serum creatinine as relevant risk factors. This study indicates that better data preparation utilizing Deep Learning with hyper parameter adjustment be used to determine the best parameter that has a substantial influence as a risk factor for heart failure. The experiments employed data from the Faisalabad Institute of Cardiology and Allied  Hospital in Faisalabad (Punjab, Pakistan), which included 299 samples. The experimental findings reveal that the proposed approach obtains a recall of 63.16% greater than related works.
Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction Hadianti, Sri; Kodri, Wan Ahmad Gazali
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.15

Abstract

Heart disease prediction is a critical area in healthcare, as early identification and accurate assessment of cardiovascular risks can lead to improved patient outcomes. This study explores the application of machine learning techniques for predicting heart disease. Various data attributes, including medical history, clinical measurements, and lifestyle factors, are utilized to develop predictive models. A comprehensive analysis of different machine learning algorithms is conducted to determine their efficacy in classification tasks. The dataset used for experimentation is sourced from a diverse patient population, enhancing the generalizability of the findings. Through rigorous evaluation and validation, the study aims to identify the most suitable machine learning approach for effectively predicting heart disease. The results highlight the potential of machine learning as a valuable tool in assisting healthcare professionals in making informed decisions and providing personalized care to individuals at risk of heart disease
Pemanfaatan Aplikasi Pengiriman Makanan Pasca Penurunan Level Pembatasan Kegiatan Masyarakat Akibat Covid-19 Di Indonesia Kodri, Wan Ahmad Gazali; Riana, Dwiza; Hadianti, Sri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106859

Abstract

Penggunaan aplikasi pengiriman makanan meningkat sangat cepat, terlebih saat terjadinya Pandemi COVID-19, dimana pergerakan orang dibatasi, membuat setiap orang berupaya menggunakan aplikasi pengiriman makanan atau Food Delivery Application (FDA) dalam memenuhi kebutuhan pangan. Penurunan jumlah kasus COVID-19 menyebabkan pemerintah Indonesia menurunkan level Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) sehingga masyarakat dapat beraktivitas sosial kembali. Tujuan penelitian ini adalah untuk menilai instrumen yang memengaruhi Continuance Intention FDA pasca penurunan level PPKM COVID-19 menjadi level 1 di Indonesia. Sebanyak 166 responden telah dikumpulkan. Kuesioner terdari dari 17 pertanyaan demografi dan 38 pertanyaan indikator. Skala Likert dengan lima tingkat penilaian digunakan untuk mengevaluasi pertanyaan indikator. Model yang digunakan adalah Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Data dianalisis dengan menggunakan Structural Equation Modeling (SEM) berbasis Partial Least Square (PLS), meliputi analisis faktor, analisis jalur, dan regresi. Penelitian menunjukkan Performance Expectancy, Social Influence, Habit, dan Rasa Solidaritas berdampak signifikan pada Continuance Intention FDA. Effort Expectancy, Facilitating Condition, Hedonic Motivation, Price Value, dan Risk Perception menunjukkan pengaruh yang tidak signifikan terhadap Continuance Intention. Pengembang FDA dapat menggunakan data ini untuk meningkatkan layanan mereka dan menambah pemahaman tentang FDA, loyalitas pengguna, peluang bisnis dan strategi pemasaran. Restoran dapat menggunakan kajian ini untuk melihat pergeseran pola pembelian makanan. AbstractThe use of food delivery applications is increasing very quickly, especially during the COVID-19 Pandemic, when people's movements were restricted, making everyone try to use Food Delivery Applications (FDA) to meet their meal needs. The decrease in the number of COVID-19 cases has caused the Indonesian government to lower the level of Enforcement of Restrictions on Community Activities (PPKM) so that people can return to common social activities. The purpose of this study was to assess the instruments that influence the FDA's Continuance Intention after the reduction in the level of PPKM COVID-19 to level 1 in Indonesia. A total of 166 respondents have been collected. The questionnaire consists of 17 demographic questions and 38 indicator questions. A Likert scale with five rating levels was used to evaluate the indicator questions. The model used is the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Data were analyzed using Partial Least Square (PLS) based Structural Equation Modeling (SEM), including factor analysis, path analysis, and regression. Research shows Performance Expectancy, Social Influence, Habit, and Sense of Solidarity have a significant impact on FDA Continuance Intention. Effort Expectancy, Facilitating Condition, Hedonic Motivation, Price Value, and Risk Perception show no significant effect on Continuance Intention. FDA developers can use this data to improve their services and increase their understanding of the FDA, user loyalty, and identify marketing opportunities and strategies. Restaurants can use this assessment to see shifts in food purchasing patterns.