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"With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions."

"Penelitian mengenai aplikasi m-Health mengungkapkan bahwa privasi dan keamanan masih menjadi isu yang penting pada aplikasi tersebut. Indonesia sendiri masih sering dihadapkan dengan insiden kebocoran data pengguna pada aplikasi m-Health. Oleh karena itu, penelitian ini bertujuan untuk mengevaluasi kepatuhan kebijakan privasi berdasarkan hukum yang berlaku, serta menganalisis sejauh mana aplikasi m-Health di Indonesia melindungi privasi pengguna dan menjaga keamanan aplikasinya ditinjau dari sisi teknis. Dilakukan penilaian kepatuhan kebijakan privasi terhadap Undang-Undang Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi (UU PDP) dan analisis keamanan aplikasi secara statis dan dinamis dengan menggunakan tools, seperti MobSF dan Fiddler (proxy), untuk mengidentifikasi celah keamanan berdasarkan standar yang ada, dalam hal ini CWE (Common Weakness Enumeration). Hasil yang didapatkan menunjukkan bahwa skor penilaian kepatuhan kebijakan privasi tertinggi hanya mencapai 65% dan setiap aplikasi setidaknya diduga memiliki satu kelemahan pada fitur keamanan yang diimplementasikan. Dengan begitu, dapat disimpulkan bahwa kebijakan privasi aplikasi m-Health di Indonesia belum sepenuhnya sesuai dengan apa yang ditetapkan dalam UU PDP dan masih banyak celah keamanan pada aplikasi m-Health yang dapat dieksploitasi.

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The research on m-Health applications reveals that privacy and security remain significant concerns in these applications. Indonesia itself frequently faces incidents of user data breaches in m-Health applications. Therefore, this study aims to evaluate compliance with privacy policies based on applicable law and analyze the extent to which m-Health applications in Indonesia protect user privacy and maintain application security from a technical perspective. The assessment includes evaluating privacy policy compliance with Undang-Undang Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi (UU PDP) and conducting static and dynamic application security analysis using tools such as MobSF and Fiddler (proxy). This analysis aims to identify security vulnerabilities based on a standard, specifically Common Weakness Enumeration (CWE). The results indicate that the highest privacy policy compliance score reaches only 65%, and each application is suspected of having at least one weakness in the implemented security features. Consequently, it can be concluded that the privacy policies of m-Health applications in Indonesia are not fully aligned with the provisions of the UU PDP. Additionally, there are still numerous security vulnerabilities in m-Health applications that could be exploited."