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Hewiz, Alya Shafira
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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Journal : journal medical informatics technology

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Performance Comparison of Three Classification Algorithms for Non-alcoholic Fatty Liver Disease Patients Using Data Mining Tool Octaviantara, Adi; Abbas, Moch Anwar; Azhari, Ahmad; Riana, Dwiza; Hewiz, Alya Shafira
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.v1i1.2

Abstract

This study aims to carry out a comparative analysis of the three classification algorithms used in research on Nonalcoholic Fatty Liver Disease (NAFLD) Patients. NAFLD is a liver condition associated with the accumulation of fat in the liver in individuals who do not consume excessive alcohol. The algorithms used in the analysis are Decision Tree, Naïve Bayes, and k-Nearest Neighbor (k-NN), with data processing using RapidMiner software. The data used is sourced from Kaggle which comes from the Rochester Epidemiology Project (REP) database with research conducted in Olmsted, Minnesota, United States. The measurement results show that the Decision Tree algorithm has an accuracy of 92.56%, a precision of 93.24%, and a recall of 99.08%. The Naïve Bayes algorithm has an accuracy of 89.93%, a precision of 95.40% and a recall of 93.56%. While the k-Nearest Neighbor algorithm has an accuracy of 91.33%, a precision of 91.94%, and a recall of 99.27%. ROC curve analysis, all algorithms show "Excellent" classification quality. However, only the k-NN algorithm reached 1.0, showing excellent classification results in solving the problem of classifying Nonalcoholic Fatty Liver Disease patients. This study concluded that the k-NN algorithm is a better choice in solving the problem of classifying Non-alcoholic Fatty Liver Disease patients compared to the Decision Tree and Naïve Bayes algorithms. This study provides valuable insights in the development of classification methods for the early diagnosis and management of NAFLD.
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.
Risk Factors and Prevalence of Urinary Incontinence in Elderly Women, a Case Study in Japan and Taiwan Hewiz, Alya Shafira; Widajanti, Novira; Hakim, Lukman; Satyawati, Rwahita
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.9

Abstract

Knowledge of the conditions of elderly women in Japanese and Taiwanese communities, particularly in relation to risk factors and their association with urinary incontinence, is of interest. This study aimed to identify risk factors and prevalence of urinary incontinence in elderly women in the community of the Japanese and Taiwanese case study areas. The research method used was a systematic review based on PRISMA guidelines. Data sources were obtained from PubMed and Science Direct for the period 2000-2020 using specific inclusion and exclusion criteria. Evaluation was conducted for quality and bias risk using a standardized assessment system. Results showed that the prevalence of urinary incontinence in elderly women in Japanese and Taiwanese communities ranged from 29.8% to 31.3%. Many factors influenced urinary incontinence, such as age, body mass index (BMI), and smoking habits. From the two selected articles in Japan and Taiwan, it was concluded that urinary incontinence was commonly experienced by elderly women in the community, and awareness of this condition could help improve management.
The Relationship between Age, Education, and Maternal Employment with Exclusive Breastfeeding in Children Aged 6 - 23 Months in Kalirejo, Malang Regency Ramadhanti, Nanda Amalia; Farid, Muhammad Rifqo Hafidzudin; Fadila, Salma; Naziliah, Adristi Hanun; Febriyani, Putu Laksmi; Gabriella, Clarisa Christina; Hewiz, Alya Shafira
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.v1i3.17

Abstract

The target percentage of infants under 6 months old receiving exclusive breastfeeding is 40%. However, in 2020, in Kalirejo Sub-district, the number was 9.04%. This was presumably caused by the high number of working mothers. Therefore, this study was conducted to analyze the relationship between age, education, and occupation of mothers regarding exclusive breastfeeding in children aged 6-23 months in Kalirejo Sub-district, Malang Regency. The study design employed in this research was observational analytics with a cross-sectional design. The research population consisted of mothers residing in Kalirejo Sub-district, Malang Regency. The required sample size was 66 individuals. A questionnaire was used to collect research data. The required data was processed using SPSS software with the Chi-Square Test analysis technique. The result showed that 78.8% mothers were in the age group of 20-35%, and 21.2% were in the age group of >35 years. Based on the highest education level attained by the respondents, 1.5% had completed elementary school, 15.2% had completed junior high school, 57.6% had completed high school, and 25.8% had completed tertiary education. About 51.5% of respondents were employed, while 48.5% were not employed. The number of respondents' children receiving exclusive breastfeeding was 50%. The analysis indicated a relationship between occupation and exclusive breastfeeding with a p-value of 0.049 and a strength of relationship between the two variables at 0.236.
Low Tuberculosis Screening among Household Family Members of Tuberculosis Patients in Banyuarang and Sidowarek Farid, Muhammad Rifqo Hafidzudin; Rananda, Arif; Aflahudin, Muhammad Ahda Naufal; Musalim, Dian Anggraini Permatasari; Hariftyani, Arisvia Sukma; Hanani, Nadya Kelfinta; Rofiq, Rodia Amanata; Aulia, Shazia Hafazhah; Sidqoh, Aida Badi’atus; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.19

Abstract

Early tuberculosis detection is vital, necessitating widespread screening. The WHO's End Tuberculosis strategy aims to combat this epidemic. Active screening is critical for identifying asymptomatic individuals at risk. Data from Pulorejo Primary Health Center, Jombang, indicates a low 10% coverage of suspected cases in 2021, particularly among household contacts, resulting in continued transmission, late detection, post-treatment symptoms, and even death. Therefore, this study was conducted to determine the number of screening participation of households of tuberculosis patients in Banyuarang and Sidowarek Village. This research is a descriptive observational. The data collected was primary data from questionnaires. The study population consisted of households of tuberculosis patients in the Banyuarang and Sidowarek Villages, Jombang Regency. Data collected from 12 respondents showed the prevalent characteristics among the 12 respondents were predominantly female, adult age, high school education, working, limited knowledge about tuberculosis, and easy access to healthcare services. Among the 12 respondents in Banyuarang and Sidowarek, 9 respondents had never been screened, while 3 respondents had undergone screening. The primary reasons for respondents not undergoing screening were lack of awareness regarding the necessity of screening and busy schedules.
Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means Dwi Saputra, Heru; Efendi, Ade Irfan Efendi; Rudini, Edwin; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.21

Abstract

Hepatitis is a serious disease that causes death throughout the world. It is responsible for inflammation in the human liver. If we manage to detect this life-threatening disease early, we can save many lives from it. In this research paper, we predict hepatitis disease using data mining techniques. We have attempted to propose a feasible approach to improve the performance of our prediction models in our research. We address the problem of missing values in the dataset by replacing them with the mean value. Nine algorithms were applied to the hepatitis disease dataset to calculate prediction accuracy. We measure accuracy, precision, recall, ROC and best score, and we compare them with random search hyperparameter tuning. It is hoped that by using them we will find the optimal combination of hyperparameters to improve the performance of machine learning models which helps us compare the performance of classification models.
Co-Authors Abbas, Moch Anwar Aflahudin, Muhammad Ahda Naufal Ahmad Azhari Aulia, Shazia Hafazhah Aziz, Faruq Dwi Saputra, Heru Dwiza Riana Efendi, Ade Irfan Efendi Fadila, Salma Febriyani, Putu Laksmi Gabriella, Clarisa Christina Hanani, Nadya Kelfinta Hariftyani, Arisvia Sukma Herwanto, Teguh Kodri, Wan Ahmad Gazali Lukman Hakim Muhammad Rifqo Hafidzudin Farid Musalim, Dian Anggraini Permatasari Naziliah, Adristi Hanun Novira Widajanti, Novira Octaviantara, Adi Ramadhanti, Nanda Amalia Rananda, Arif Rofiq, Rodia Amanata Rudini, Edwin Rwahita Satyawati Dharmanta Sidqoh, Aida Badi’atus