A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PD

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PD

A Comparative Study of Collaborative Filtering in Product Recommendation

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PD

Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis

Services of data interoperability applied to heterogeneous healthcare infrastructures and applications

In the last decade, there has been a transition from a data-poor to a data-rich world, with the aim of improving the quality of transport, governance, environment, communication and health. Much of this unprecedented increase in data generation can be attributed to the abundance of thousands of mobile devices, wearables, and sensors. Such thing results into a myriad of heterogeneous devices that will be connected to the world of Internet of Things (IoT), producing data of different types that may have been collected at different locations and time scales, by different devices. Most of these devices are typically characterized by a high degree of heterogeneity, in terms of having different characteristics, capabilities, or network specifications, needing to be easily manageable in particular with regard to the management of the heterogeneous data they generate. Especially for the Internet of Medical Things (IoMT) devices that are widely adopted and used in Healthcare 4.0, the need for using, understanding, and processing of these devices’ data is an issue of vital importance. However, with regard to the best use of this data, heterogeneity, complexity, noise and imperfection are among their most common challenges. It is undeniable that vast amounts of heterogeneous medical data are becoming available in various healthcare organizations and devices, thus completely reshaping the Healthcare 4.0. In order for these heterogeneous medical data to be exchanged with as many stakeholders as possible, and to be a key driver of providing personalized and efficient medical care to patients and citizens, interoperability is the only way. Additionally, the quality of the healthcare services can be improved, health costs and medical errors can be reduced, and patients’ privacy can be increased, while public health can be completely renewed.Currently, the rapidly increasing availability of health records is pushing towards the adoption of data-driven approaches, bringing the opportunities for more accurate disease and symptom prognosis, to automate healthcare related tasks, providing better disease detection, and more efficient clinical research. Nevertheless, the healthcare organizations are still facing many difficulties in implementing, maintaining and upgrading their healthcare systems, including many challenges in the technical, security and human interaction fields. Among others, what is missing is an integrated data exchange system for which, in order to exchange data with as many stakeholders as possible to improve public health, interoperability is the only way for letting systems interact with each other. Having in mind that the wearable medical devices market is expected to quadruple, and that hospitals and doctors’ offices nationwide could dramatically improve their patients’ quality of life, such thing results into making the health interoperability task even more daunting. For this purpose, many standardization techniques are annually invested in health data interoperability. Among the different existing researches and standards, the Health Level Seven (HL7) organization provides the development and the framework of standards that are widely used in the medical market and research, with HL7 Fast Healthcare Interoperability Resources (FHIR) being the latest standard created by this organization, for the exchange of clinical information.The current PhD dissertation aims to create an Interoperability Services’ approach in the form of an automated transformation of heterogeneous medical data deriving from heterogeneous devices. This approach will transform both semantically and syntactically health data of different representation and morphology, in order to identify, match into a single language, and eventually merge it into a common level. For this reason, the mechanisms that were studied more are those that aim in the interoperable exchange of health data and information, between systems belonging to different groups or under different health standards. Following this research, an approach was developed which was able to separate health data into categories according to how they were modelled, and then exporting knowledge, information and metadata, the data was translated into a single language, which retained combined data from other already-standardized health data representation languages. Subsequently, this translation approach was adapted to generate ontologies through specific metamodel layers and ontologies, with the ultimate goal of jointly rendering the data in a single format. Using the created ontologies, the next approach was to create ontologies and to compare them with individual HL7 FHIR resources’ ontologies due to its worldwide dissemination and adoption. This comparison concerned both the syntactic and the semantic mapping of the ontologies so that the ontologies of the health data can be mapped to the ontologies of the HL7 FHIR resources. The final approach was therefore based on the above-mentioned solution, where both the ontology implementation mechanisms, but also the syntactic and semantic comparison and mapping, were improved based on relevant experimental tests.Focusing on the healthcare domain, most of the existing health data interoperability practices and techniques are based on health standards as well as mechanisms for ontological transformations and mappings. However, these are designed for specific cases of use with the pre-defined format in which the data must be entered without having adaptability, without the ability to be considered as holistic solutions. This gap is covered by the current PhD dissertation, delivering a unified and generalized approach that can be applied automatically to any set of health data without the need for pre-processing, assigning and matching this data to one of the most powerful and promising health standards, the HL7 FHIR. The results of the experimental tests listed below, evaluate and demonstrate the operation and efficiency of the proposed approach, making it possible to use it for integration into multiple sectors and environments, particularly in the field of health care, as well as in the fields of telecommunications and networks, devices’ identification, e-Government and e-Procurement.Την τελευταία δεκαετία, υπήρξε μια μετάβαση από έναν περιορισμένο σε δεδομένα κόσμο σε έναν πλούσιο σε δεδομένα κόσμο με στόχο τη βελτίωση της ποιότητας του τομέα των μεταφορών, της διακυβέρνησης, του περιβάλλοντος, της επικοινωνίας και της υγείας. Μεγάλο μέρος αυτής της άνευ προηγουμένου αύξησης της παραγωγής δεδομένων μπορεί να αποδοθεί στην παρουσία πολλών κινητών συσκευών, φορητών συσκευών και αισθητήρων. Αυτό έχει ως αποτέλεσμα ένα πλήθος συσκευών που έχουν τη δυνατότητα σύνδεσης με το διαδίκτυο, παράγοντας δεδομένα διαφορετικών τύπων που μπορεί να έχουν συλλεχθεί σε διαφορετικές χρονικές στιγμές και τοποθεσίες, από ετερογενείς συσκευές. Οι περισσότερες από αυτές τις συσκευές τυπικά χαρακτηρίζονται από υψηλό βαθμό ετερογένειας, όσον αφορά την ύπαρξη διαφορετικών χαρακτηριστικών, δυνατοτήτων ή προδιαγραφών δικτύου, τα οποία πρέπει να είναι αποδοτικά διαχειρίσιμα αναφορικά κυρίως με τη διαχείριση των ετερογενών δεδομένων που παράγουν. Ειδικά για τις ιατρικές συσκευές - Internet of Medical Things (IoMT), οι οποίες υιοθετούνται ευρέως και χρησιμοποιούνται στην 4η Βιομηχανική Επανάσταση του χώρου της Υγείας (Healthcare 4.0), η ανάγκη χρήσης, κατανόησης και επεξεργασίας των δεδομένων τους χρήζει ως ένα ζήτημα ζωτικής σημασίας. Αναφορικά ωστόσο με τη βέλτιστη αξιοποίηση των δεδομένων αυτών, η ετερογένεια, η πολυπλοκότητα, ο θόρυβος και η ατέλειά τους, συγκαταλέγονται στις πιο κοινές τους προκλήσεις. Είναι εμφανές πως μεγάλα ποσά ετερογενών ιατρικών δεδομένων καθίστανται διαθέσιμα σε διάφορους οργανισμούς και συσκευές υγειονομικής περίθαλψης, αναμορφώνοντας έτσι πλήρως το χώρο του Healthcare 4.0. Προκειμένου αυτά τα ετερογενή ιατρικά δεδομένα να ανταλλάσσονται με όσο το δυνατόν περισσότερους ενδιαφερόμενους και να αποτελούν βασικό γνώμονα παροχής εξατομικευμένης και αποδοτικής ιατρικής φροντίδας προς τους ασθενείς και τους πολίτες, η διαλειτουργικότητα είναι ο μόνος τρόπος. Σαν πρόσθετα αποτελέσματα, μπορεί να αυξηθεί η ποιότητα των υπηρεσιών υγείας, να μειωθεί το κόστος της και τα ιατρικά σφάλματα, να αυξηθεί η ιδιωτικότητα των ασθενών, και να ανανεωθεί ολοκληρωτικά η δημόσια υγεία.Επί του παρόντος, η ταχέως αυξανόμενη διαθεσιμότητα των ιατρικών αρχείων ωθεί στην υιοθέτηση προσεγγίσεων που βασίζονται σε δεδομένα, όπως η ακριβέστερη πρόγνωση ασθενειών και συμπτωμάτων, η αυτοματοποίηση των καθηκόντων που σχετίζονται με την υγειονομική περίθαλψη, η ευκολότερη ανίχνευση ασθενειών, και η αποδοτικότερη κλινική έρευνα. Παρόλα αυτά, οι οργανισμοί υγειονομικής περίθαλψης εξακολουθούν να αντιμετωπίζουν πολλές δυσκολίες στην εφαρμογή, στη συντήρηση και στην αναβάθμιση των συστημάτων υγειονομικής περίθαλψης, συμπεριλαμβανομένων πολλών προκλήσεων στους τομείς της τεχνολογίας, της ασφάλειας και της ανθρώπινης επικοινωνίας. Μεταξύ άλλων, μια πρόσθετη πρόκληση αναφέρεται στην έλλειψη μιας ολοκληρωμένης προσέγγισης ανταλλαγής δεδομένων, τα οποία προκειμένου να ανταλλάσσονται με όσο το δυνατόν περισσότερους ενδιαφερόμενους ώστε να βελτιώσουν τη δημοσία υγεία, απαιτούν την ύπαρξη της διαλειτουργικότητας, η οποία αποτελεί το βασικό μέσο για να δοθεί η δυνατότητα στα συστήματα να αλληλοεπιδρούν μεταξύ τους. Έχοντας κατά νου ότι η αγορά φορητών ιατρικών συσκευών αναμένεται να τετραπλασιαστεί, και πως τα νοσοκομεία και τα ιατρεία σε εθνικό επίπεδο θα μπορούσαν να βελτιώσουν δραματικά την ποιότητα ζωής των ασθενών, αυτό έχει ως αποτέλεσμα να καταστεί ακόμη πιο δύσκολο και επιτακτικό το έργο της διαλειτουργικότητας στον τομέα της υγείας. Για το σκοπό αυτό, πολλές τεχνικές τυποποίησης αλλά και έρευνες επενδύονται ετησίως στη διαλειτουργικότητα των δεδομένων για την υγεία. Μεταξύ των διαφόρων υφιστάμενων ερευνών και προτύπων, ο οργανισμός (Health Level 7) HL7 παρέχει την ανάπτυξη και το πλαίσιο των προτύπων που χρησιμοποιούνται ευρέως στην ιατρική αγορά και την έρευνα, με το HL7 Fast Healthcare Interoperability Resources (FHIR) να είναι το πιο πρόσφατο πρότυπο που δημιουργήθηκε από τον οργανισμό για την ανταλλαγή κλινικών πληροφοριών.Βάσει όλων των προαναφερθέντων υφιστάμενων προκλήσεων, η παρούσα Διδακτορική Διατριβή στοχεύει στη δημιουργία μιας προσέγγισης Υπηρεσιών Διαλειτουργικότητας Δεδομένων σε μορφή ενός αυτοματοποιημένου μετασχηματισμού ανομοιογενών δεδομένων υγείας από ετερογενείς πηγές δεδομένων. Η προσέγγιση αυτή είναι ικανή να μετασχηματίζει τόσο σε σημασιολογικό όσο και σε συντακτικό επίπεδο δεδομένα διαφορετικής αναπαράστασης και μορφής, προκειμένου αυτά να μπορούν να ταυτοποιούνται, να αντιστοιχίζονται σε μία ενιαία γλώσσα, και στο τέλος να συγχωνεύονται σε ένα κοινό επίπεδο. Για να επιτευχθεί αυτό, αρχικά έγινε εμβάθυνση στους μηχανισμούς που έχουν υλοποιηθεί ώστε τα δεδομένα υγείας και οι πληροφορίες να μπορούν να ανταλλάσσονται διαλειτουργικά μεταξύ συστημάτων που ανήκουν σε διαφορετικές ομάδες ή υπακούν σε διαφορετικά πρότυπα υγείας. Κατόπιν σχετικής έρευνας, προτάθηκε μία προσέγγιση η οποία είναι ικανή να διαχωρίζει τα δεδομένα υγείας σε κατηγορίες ανάλογα με τον τρόπο αναπαράστασής τους, και στη συνέχεια εξάγοντας γνώσεις, πληροφορίες και μεταδεδομένα, τα δεδομένα μετασχηματίζονται σε μία ενιαία γλώσσα, η οποία διατηρεί συνδυαστικά στοιχεία από άλλες ήδη προτυποποιημένες γλώσσες αναπαράστασης δεδομένων υγείας. Εν συνεχεία, η συγκεκριμένη προσέγγιση μετασχηματισμού προσαρμόστηκε ώστε να παράγει οντολογίες μέσω συγκεκριμένων επιπέδων μεταμοντέλου και οντολογιών, με απώτερο στόχο την κοινή αναπαράσταση των δεδομένων σε μία ενιαία μορφή. Χρησιμοποιώντας ως γνώμονα τις παραχθείσες οντολογίες, η προσέγγιση που ακολουθήθηκε εστίασε αφενός στη δημιουργία των οντολογιών, και αφετέρου στη σύγκρισή τους με τις οντολογίες των επιμέρους πόρων (resources) του προτύπου υγείας HL7 FHIR, λόγω της παγκόσμιας διάδοσης και υιοθέτησής του. Η σύγκριση αυτή αφορούσε τόσο τη συντακτική όσο και τη σημασιολογική απεικόνιση των εκάστοτε οντολογιών, ώστε στο τέλος να καθίσταται εφικτή η αντιστοίχιση των οντολογιών των δεδομένων υγείας με τις οντολογίες των πόρων HL7 FHIR. Η τελική προσέγγιση βασίστηκε στην προαναφερθείσα λύση, όπου τόσο οι μηχανισμοί υλοποίησης των οντολογιών όσο και της συντακτικής και της σημασιολογικής σύγκρισης και αντιστοίχισης βελτιώθηκαν, βάσει σχετικών πειραματικών δοκιμών.Εστιάζοντας στο χώρο της υγείας, οι περισσότερες από τις ήδη υπάρχουσες πρακτικές και τεχνικές διαλειτουργικότητας δεδομένων στηρίζονται σε πρότυπα υγείας όπως και σε μηχανισμούς οντολογικών μετασχηματισμών και αντιστοιχίσεων. Ωστόσο είναι σχεδιασμένες για συγκεκριμένες περιπτώσεις χρήσης με ήδη προκαθορισμένη τη μορφή με την οποία πρέπει να εισέρχονται τα δεδομένα, χωρίς να έχουν τη δυνατότητα προσαρμογής, μη μπορώντας έτσι να θεωρηθούν ως ολιστικές λύσεις. Η συγκεκριμένη πρόκληση απαντάται από την παρούσα Διδακτορική Διατριβή, αποδίδοντας μια ενιαία και γενικευμένη προσέγγιση η οποία μπορεί να εφαρμοστεί αυτοματοποιημένα σε οποιοδήποτε σύνολο δεδομένων υγείας, χωρίς την ανάγκη προ-επεξεργασίας τους, αποδίδοντας και αντιστοιχίζοντάς τα σε ένα από τα πιο ισχυρά πρότυπα υγείας, το HL7 FHIR. Τα αποτελέσματα των πειραματικών δοκιμών που πραγματοποιήθηκαν αξιολογούν και αποδεικνύουν τη λειτουργικότητα και αποδοτικότητα της προτεινόμενης προσέγγισης, καθιστώντας την εφικτή για χρήση και ενσωμάτωση σε πολλαπλούς τομείς και περιβάλλοντα, και ειδικότερα στο χώρο της υγειονομικής περίθαλψης, καθώς και στους τομείς των τηλεπικοινωνιών και των δικτύων, της αναγνώρισης συσκευών, της ηλεκτρονικής διακυβέρνησης και των ηλεκτρονικών προμηθειών

EverAnalyzer: A Self-Adjustable Big Data Management Platform Exploiting the Hadoop Ecosystem

Big Data is a phenomenon that affects today’s world, with new data being generated every second. Today’s enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users’ processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform’s efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments

EverAnalyzer: A Self-Adjustable Big Data Management Platform Exploiting the Hadoop Ecosystem

Big Data is a phenomenon that affects today’s world, with new data being generated every second. Today’s enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users’ processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform’s efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy

HealthFetch: An Influence-Based, Context-Aware Prefetch Scheme in Citizen-Centered Health Storage Clouds

Over the past few years, increasing attention has been given to the health sector and the integration of new technologies into it. Cloud computing and storage clouds have become essentially state of the art solutions for other major areas and have started to rapidly make their presence powerful in the health sector as well. More and more companies are working toward a future that will allow healthcare professionals to engage more with such infrastructures, enabling them a vast number of possibilities. While this is a very important step, less attention has been given to the citizens. For this reason, in this paper, a citizen-centered storage cloud solution is proposed that will allow citizens to hold their health data in their own hands while also enabling the exchange of these data with healthcare professionals during emergency situations. Not only that, in order to reduce the health data transmission delay, a novel context-aware prefetch engine enriched with deep learning capabilities is proposed. The proposed prefetch scheme, along with the proposed storage cloud, is put under a two-fold evaluation in several deployment and usage scenarios in order to examine its performance with respect to the data transmission times, while also evaluating its outcomes compared to other state of the art solutions. The results show that the proposed solution shows significant improvement of the download speed when compared with the storage cloud, especially when large data are exchanged. In addition, the results of the proposed scheme evaluation depict that the proposed scheme improves the overall predictions, considering the coefficient of determination (R2 > 0.94) and the mean of errors (RMSE < 1), while also reducing the training data by 12%

A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario’s requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios’ nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism’s efficiency