Depok, August 9, 2022. Statistics is a science related to data. Over time, statistics has become a tool to inform decision-making processes in the face of uncertainty in both the sciences and humanities. This is because statistics encompasses the concepts of randomness, variability, error, and probability. Statistics has begun to develop into a combination of disciplines such as data science and biostatistics.
Prof. Dr. Dra. Titin Siswantining, DEA, through her research entitled “The Role of Statistics in Data Science in Predicting Intelligence Healthcare Welcoming the Era of Society 5.0”, revealed that data science emerged as a combination of science and social sciences. The sciences that are the main supports in data science consist of mathematics, statistics, computer science, information systems, management, and communication science. Data science uses statistics to collect, review, analyze, and draw conclusions from data, as well as apply measured mathematical models to appropriate variables.
Meanwhile, Society 5.0 is a concept that defines how technology and humans will coexist to sustainably improve the quality of human life. Data science is at the root of this technology. Its benefits can be felt in various fields, including healthcare. The role of statistics in healthcare is aided by machine learning methods.
Machine learning is a field of science that develops algorithms or models that can extract knowledge from data, much like human learning. Machine learning can be used to replace humans, especially when processing large, complex data sets that require rapid responses, such as in the healthcare industry.
Healthcare phenomena involving technology and digitalization for data collection, processing, and prediction are called intelligent healthcare. When utilizing statistics and machine learning in healthcare, it is highly recommended to ensure the data is ready for processing and not. This is because the data often contains incomplete, illegible, or outliers, meaning values are too low or too high.
Intelligent healthcare encompasses the application of machine learning in healthcare, integrating it with care management processes, utilization, and addressing the needs of target populations. The concrete role of statistics and data science lies in the application of clustering, prediction, and data imputation methods. The data input for intelligent healthcare applications varies, including microarray data, DNA sequences, CT scans, patient data, and protein interaction data.
Machine learning dibagi menjadi supervised learning, unsupervised learning, dan reinforcement learning. Penelitian bidang kesehatan menggunakan metode machine learning sub-bidang supervised learning, antara lain Classification of Diabetic Retinopathy Stages Using Histogram of Oriented Gradients and Shallow Learning (2018); Feature Selection Using Random Forest Classifier for Predicting Prostate Cancer (2019); Ovarian Cancer Classification Using Bayesian Logistic Regression (2019); Multiclass Classification of Acute Lymphoblastic Leukemia Microarrays Data Using Support Vector Machine Algorithms (2020); Kernel PCA and SVM-RFE Based Feature Selection for Classification of Dengue Microarray (2020); dan Covid-19 Classification Using X-Ray Imaging with Ensemble Learning (2021).
Meanwhile, research related to unsupervised learning is conducted through clustering, a method for grouping unlabeled data. Clustering has evolved into biclustering and triclustering. Biclustering is a data mining technique that allows for simultaneous grouping of rows and columns of a matrix. Triclusters, on the other hand, are constructed from two datasets by selecting a subset of features from each dataset and a subset of rows shared among all rows. Triclustering is an extension of the clustering and biclustering methods that works on three-dimensional data.
The study, “Triclustering Method for Finding Biomarkers in Human Immunodeficiency Virus-1 Gene Expression Data,” reflects the role of statistics in data science in predicting intelligent healthcare in the era of Society 5.0. In general, Society 5.0 is a combination of IoT (Internet of Things), Big Data, and AI (Artificial Intelligence). Biostatistics is a tool for predicting a patient’s Intelligent Healthcare in a hospital.
“We hope that in the future, a research platform or incubator and a Biostatistics laboratory will be created within the Faculty of Mathematics and Natural Sciences, University of Indonesia (FMIPA UI) as a pioneer of Artificial Intelligence–Quality Improvement (AI-QI) in Indonesia to welcome the era of society 5.0 that we will face,” said Prof. Titin.
Following the speech, Prof. Titin was officially inaugurated as a Permanent Professor of Statistics at the Faculty of Mathematics and Natural Sciences (FMIPA), University of Indonesia (UI). The inauguration was led by UI Rector, Prof. Ari Kuncoro, SE, MA, Ph.D., and broadcast live virtually on the UI Television YouTube channel.
The event held on Saturday (6/8) was attended by invited guests, including the Commander of Iskandar Muda Military Command, Major General TNI Moh. Hasan; Head of the Social Welfare Data and Information Center, Prof. Dr. Agus Zainal Arifin, S.Kom., M.Kom.; Head of the PAK UI Adhoc Team, Prof. Heru Suhartanto, Drs, M.Sc., Ph.D.; UGM Professor, IndoMS Advisor, Prof. Dr. Sri Wahyuni, S.U.; CEO of Global Risk Management (GRM), Rinaldi Anwar, S.Si, MM, FSAI; Ph.D. Supervisor, Professor Queensland University of Technology (QUT), Australia, Prof. Dr. Kevin Burrage; Professor Perdana University (Malaysia), Prof. Mohammad Asif Khan, Ph.D.; UNPAD Professor, IndoMS Advisor, Prof. Dr. Budi Nurani Ruchjana, MS; Head of SAU UNAND, Prof. Dr. Syafrizal; and Professor of ITB, Indonesian Biomathematics Association, Prof. Edy Soewono, Ph.D.
Prof. Dr. Dra. Titin Siswantining, DEA is a lecturer in the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Indonesia. She completed her studies in Statistics Information System (SI) at the Sepuluh Nopember Institute of Technology, Surabaya (ITS) in 1984; DEA en Mathematique Applique, EHESS – Universite de Paris V in 1990; and her Doctorate in Statistics from the Bogor Agricultural University (IPB) in 2013.
Several published scientific works, namely Pathway-Based Triclustering and Gene Onthology in Analyzing Gene Sample Time in Cancer Data (2022); Genomic Study with The Application of Triclustering Algorithm to Predict Chronic Diseases Using Machine Learning Method (2020/2021); Parallel Clustering Algorithms and Implementations for Big Data Analytic (2020/2021); Implementation of 3D Microarray Gene Expression Data using δ-Trimax, EDISA, and OPTricluster Algorithms (2020/2021); and Computer-Aided Diagnosis (CAD) for Early Detection of Diabetic Retinopathy (2020/2021).
