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Nair, Mydhili K.
- Survey of Classification Based Prediction Techniques in Healthcare
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Authors
Affiliations
1 Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru - 560064, Karnataka, IN
2 Department of Information Science & Engineering, M. S. Ramaiah Institute of Technology, Bengaluru - 560054, Karnataka, IN
1 Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru - 560064, Karnataka, IN
2 Department of Information Science & Engineering, M. S. Ramaiah Institute of Technology, Bengaluru - 560054, Karnataka, IN
Source
Indian Journal of Science and Technology, Vol 11, No 15 (2018), Pagination:Abstract
Data mining is used extensively and is applied successfully in various fields like market-basket analysis, e-business, fraud detection, quality control, cross-selling of products etc. More recently, data mining has been successfully applied to healthcare sector and healthcare applications. Objectives: The objective of this research is to study the classification based prediction techniques as applied to healthcare. It also aims at finding the different applications and tools used in classification based prediction in the healthcare sector. Methods: Prevalently the prediction techniques used are Decision Trees, Naive Bayes classifier, Bayesian networks, k-Nearest neighbour and artificial neural networks. A few researchers also have used support vector machines, genetic algorithm and decision rules for prediction. Feature selection techniques have been applied to extract relevant features required for the purpose of prediction. Findings: It is found that there is no single algorithm or technique that is the best of all the other algorithms/technique on any given medical dataset and application. Always there is a need to explore the right technique for the given dataset. A detailed review of the research on classification based prediction techniques reveal that the algorithms and techniques are applied on different data sets, which also has heterogeneous data types. It is observed that work is done on improving the predictive accuracy by applying attribute selection measures and feature selection techniques. Techniques have been developed to diagnose diseases, predict the occurrence of diseases, assess the gravity of the diseases such as cancer, heart, skin, liver, SARS, diabetes to name a few. The various applications explored are SMARTDIAB, H-Cloud, Medical Decision Support System, Evidence based medicine, adverse drug events, Passive In-home Health and Wellness monitoring, Healthcare management are a few applications developed in support of Medical data mining. Application: SMARTDIAB is an automated system for monitoring and management of type 1 Diabetic patients which supports monitoring, management and treatment of patients with type 1 diabetes. Passive In-home health and wellness monitoring is an application for monitoring older adults passively in their own living settings through placing sensors in their living environment.Keywords
Bayesian, Challenges, Classification, Data Mining, Decision Tree, Healthcare, SurveyFull Text
- A Robust Fault-Tolerant and QoS Centric Routing Protocol for Mobile-WMSNs:FTQ-RPM
Abstract Views :217 |
PDF Views:0
Authors
A. Ajina
1,
Mydhili K. Nair
2
Affiliations
1 Department of Computer Science and Engineering, Sir M Visvesvaraya Institute of Technology, Bangalore – 562157, Karnataka, IN
2 Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bangalore - 560054, Karnataka, IN
1 Department of Computer Science and Engineering, Sir M Visvesvaraya Institute of Technology, Bangalore – 562157, Karnataka, IN
2 Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bangalore - 560054, Karnataka, IN
Source
Indian Journal of Science and Technology, Vol 11, No 41 (2018), Pagination: 1-20Abstract
Objectives: In this study we developed a routing protocol named Fault-Tolerant QoS Centric Routing Protocol for WMSN (FTQ-RPM) where the classical RPL routing protocol was augmented with mobility features and multiple network condition parameters based parent node selection for forwarding path selection. Methods/Statistical Analysis: Fault-resilient routing protocol for Low power Lossy Networks (LLNs) which is applied in parallel to the link layer that once detecting any link outage initiates node discovery. The use of Received Signal Strength Indicator (RSSI) and Expected Number of Control Packets (ETX) based best parent node selection makes FTQ-RPM to achieve fault-resilient routing decision. In addition, RSSI based mobility management or mobile node positioning makes data communication more reliable than the random mobility. It assures reliable data communication over mobile-RPL based WMSNs. FTQ-RPM applies a global link repair model and supplementary forwarding path selection that works in parallel to the link layer of the native-RPL. It assures timely data delivery through supplementary path without imposing any additional computational overheads, delay and energy exhaustion during any link-outage condition. Findings: The overall developed routing protocols have been examined in terms of throughput, real-time data delivery, packet loss, delay, power consumption, resource utilization etc, where the proposed systems have been found superior than state-of-art existing protocols. Application/Improvements: It affirms the suitability of the proposed routing protocols for real-time WMSNs applications particularly for IoT.Full Text
References
- Akyildiz IF, Melodia T, Chowdhury KR. A survey on wireless multimedia sensor networks. Computer Networks. 2007; 51(4):921–60. https://doi.org/10.1016/j.comnet. 2006.10.002.
- Guinard D, Trifa V, Pham T, Liechti O. Towards physical mashups in the Web of Things. Proc INSS'09; 2009. p. 1–4. https://doi.org/10.1109/INSS.2009.5409925.
- Web of Things. 2017. https://webofthings.org/2017/04/08/what-is-the-web-of-things/
- M. Geoff. The 6LoWPAN architecture. Proc ACM EmNets'07; 2007. p. 78–82..
- James PF, How C, Bull D, Nix A. Distortion-based link adaptation for wireless video transmission. EURASIP Journal of Advance Signal Processing; 2008. p. 1–17.
- Schaar MV, Turaga DS. Cross-layer packetization and retransmission strategies for delay-sensitive wireless multimedia transmission. IEEE Transaction of Multimedia. 2007; 9(1):185–97.
- Fallah YP, Nasiopoulos P, Alnuweiri H. Efficient transmission of H.264 video over multirate IEEE 802.11e WLANs. EURASIP Journal of Wireless Communication and Networking. 2008.
- Heinzelman W, Kulik J, Balakrishnan H. Adaptive protocols for information dissemination in Wireless Sensor Networks. Proc of the 5th ACM/IEEE Mobicom Conference (MobiCom '99); Seattle, WA. 1999. p. 174–85. https://doi.org/10.1145/313451.313529.
- Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks. Proc of the ACM MobiCom '00; Boston, MA. 2000. p. 56–67.
- Yao Y, Gehrke J. The cougar approach to in-network query processing in sensor networks. ACM SIGMOD Record. 2002; 31(3):9–18. https://doi.org/10.1145/601858.601861.
- Heinzelman W, Chandrakasan A, Balakrishnan H. Energyefficient communication protocol for Wireless Microsensor Networks. Proc of the 33rd Hawaii International Conference on System Sciences (HICSS '00); 2000. p. 1–10. https://doi.org/10.1109/HICSS.2000.926982.
- Lindsey S, Raghavendra C. PEGASIS: Power-Efficient Gathering in Sensor Information Systems. Proc of the IEEE Aerospace Conference. 2002; 3:1125–30.
- Winter T, Thubert P, Brandt A, Clausen T, Hui J, Kelsey R, Levis P, Pister K, Struik R, Vasseur J. RPL: IPv6 Routing Protocol for Low power and Lossy Networks. RFC 6550, IETF ROLL WG. 2012.
- Vasseur JP. Terminology in Low power and Lossy Networks, draftietf-roll-terminology-06.txt. 2011. p. 1–8.
- Lee KC, Sudhaakar R, Ning J, Dai L, Addepalli S, Vasseur JP, Gerla M. A Comprehensive evaluation of RPL under mobility. Hindawi Publishing Corporation. International Journal of Vehicular Technology. 2012; 2012:1–10. https://doi.org/10.1155/2012/904308.
- Vasseur J, Dunkels A. Interconnecting smart objects with IP: The Next Internet, Morgan Kaufmann, 1 ed. 2010. p. 1–432.
- Amendment 8 Medium Access Control (MAC) Quality of Service (QoS) Enhancements. 2005. https://ieeexplore.ieee.org/document/1541572.
- Chen M, Leung VC, Mao S, Yuan Y. Directional geographical routing for real-time video communications in Wireless Sensor Networks. Computer Communication. 2007; 30(7):3368–83. https://doi.org/10.1016/j.comcom.2007.01.016.
- Oldewurtel F, Ansari J, Mahonen P. Cross-layer design for distributed source coding in Wireless Sensor Networks. Proc SENSORCOM' 08; 2008. p. 435–43. https://doi.org/10.1109/SENSORCOMM.2008.10.
- Amala Shiny VA, Nagarajan V. Energy efficient routing protocol for Mobile Wireless Sensor Network. International Journal of Computer Applications. 2012; 43(21):1–5.
- Shiang HP, Var der Schaar M. Distributed resource management in multi-hop cognitive radio networks for delay sensitive transmission. IEEE Transactions on Vehicular Technology. 2009; 58(2):941–53. https://doi.org/10.1109/TVT.2008.925308.
- Frossard P, De Martin JC, Civanlar R. Media streaming with network diversity. Proc IEEE. 2008; 96(1):39–53. https://doi.org/10.1109/JPROC.2007.909876.
- Othman JB, Yahya B. Energy efficient and QoS based routing protocol for Wireless Sensor Networks. Journal of Parallel and Distributed Computing. 2010; 70(8):849–57. https://doi.org/10.1016/j.jpdc.2010.02.010.
- Saxena N, Roy A, Shin J. Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks. Computer Networks. 2008; 52(13):2532–42. https://doi.org/10.1016/j.comnet.2008.05.009.
- Farrag O, Younis M, Amico WD. MAC support for wireless multimedia sensor networks. Proc IEEE GLOBECOM'09; 2009. p. 3781–6. https://doi.org/10.1109/GLOCOM.2009.5425242.
- Kimand H, Min SG. Priority-based QoS MAC protocol for Wireless Sensor Networks. Proc IEEE IPDPS'09; 2009. p. 1–8.
- Schaar MV, Turaga DS. Cross-layer packetization and retransmission strategies for delay-sensitive wireless multimedia transmission. IEEE Transacton Multimedia. 2007; 9(1):185–97. https://doi.org/10.1109/TMM.2006.886384.
- Jaradat T, Benhaddou D, Balakrishnan M, Al-Fuqaha A. Energy efficient cross-layer routing protocol in Wireless Sensor Networks based on fuzzy logic. 9th International Wireless Communications and Mobile Computing Conference (IWCMC); Sardinia. 2013. p. 177–82. https://doi.org/10.1109/IWCMC.2013.6583555.
- Brar GS, Rani S, Chopra V, Malhotra R, Song H, Ahmed SH. Energy efficient direction-based PDORP routing protocol for WMSN. IEEE Access. 2016; 4:3182–94. https://doi.org/10.1109/ACCESS.2016.2576475.
- Tanwer A, Hussain M, Singh Reel P. Energy efficient wireless sensing framework to enhance mobile learning. International Conf on Tech for Education; 2010. p. 50–5.
- Yahya B, Ben-Othman J. An energy efficient and QoS aware multipath routing protocol for Wireless Sensor Networks. Local Computer Networks, IEEE 34th Conference; 2009. p. 93–100.
- Ben Saad L, Chauvenet C, Tourancheau B. Simulation of the RPL routing protocol for IPv6 sensor networks: Two cases studies. International Conference on Sensor Technologies and Applications SENSORCOMM; 2011. p. 1–4.
- Ben Saad L, Tourancheau B. Sinks mobility strategy in IPv6- based WSNs for network lifetime improvement. International Conference on New Technologies, Mobility and Security (NTMS) IFIP; Paris, France. 2011. p. 1–5.
- Korbi E, Ben Brahim M, Adjih C, Saidane LA. Mobility enhanced RPL for Wireless Sensor Networks. Third International Conference on the Network of the Future (NOF); Gammarth. 2012. p. 1–8.
- Gaddour O, Koubaa A, Rangarajan R, Cheikhrouhou O, Tovar E, Abid M. Co-RPL: RPL routing for mobile low power Wireless Sensor Networks using Corona mechanism. Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems; 2014. p. 200–9. https://doi.org/10.1109/SIES.2014.6871205.
- Somaa F, Korbi IE, Adjih C, Saidane LA. A modified RPL for Wireless Sensor Networks with Bayesian inference mobility prediction. International Wireless Communications and Mobile Computing Conference (IWCMC); 2016. p. 690–5. https://doi.org/10.1109/IWCMC.2016.7577140.
- Lohith YS, Narasimman TS, Anand SVR, Hedge M. Link Peek: A link outage resilient IP packet forwarding mechanism for 6LoWPAN/RPL based Low power and Lossy Networks (LLNs). IEEE International Conference on Mobile Services; 2015. p. 65–72. https://doi.org/10.1109/MobServ.2015.19.
- Lee C. RPL under mobility. Proceeding of the IEEE Consumer Communications and Networking Conference (CCNC). 2012.
- Gungor VC, Hancke GP. Industrial Wireless Sensor Networks: Applications, protocols, and standards. CRC Press, Inc., Boca Raton, FL, USA, 1st ed. 2013.
- Raman B, Chebrolu K, Madabhushi N, Gokhale DY, Valiveti PK, Jain D. Implications of link range and (in) stability on sensor network architecture. WiNTECH '06, ACM; New York, USA. 2006. p. 65–72.
- Srinivasan K, Levis P. Rssi is under appreciated. Proceedings of the Third Workshop on Embedded Networked Sensors, EmNets; 2006.
- Farooq O, Kunz T. Contiki-based IEEE 802.15.4 channel capacity estimation and suitability of its CSMA-CA MAC layer protocol for real-time multimedia applications. Hindawi Publishing Corporation Mobile Information Systems. 2015; 2015:1–9. PMid: 25625082 PMCid: PMC4296390.