Abstract
The role of a healthcare practitioner is to diagnose a disease and find an optimum means for suitable treatment. This has been the most challenging task over the years. The researchers have been developing intelligent tools for providing support in taking medical decision. The application of AI in different health scenario strengthen the mechanism for finding a better treatment plan. The authors share some recent advancements in this domain. The role of artificial intelligence in Indian healthcare system has also been discussed. The paper analyzes the use of different AI techniques like fuzzy logic, Artificial Neural Networks, Particle Swarm Optimization and Fuzzy Neural in critical health scenario. A detail literature review has been provided in this context. The disease taken into consideration are cancer, TB, diabetes, malaria, orthopedics, obesity and disease specific to elderly people. The purpose of this article is to find the relevance of various techniques of AI in different critical health scenarios. A comparative analysis is done based on the publications since 1995. The challenges and risks associated with the usage of AI in healthcare has been analysed and suggestions made for making the analysis in the health domain more accurate and effective. Further the concept of deep learning has also been explained and its inculcation with the medical domain is discussed.
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Ahmed J, Alam A, Mobin A (2016) 5th international conference on relaibility, infocom technologies and optimization (trends and future directions) (ICRITO), 7–9 Sept 2016, Noida, India
AI-Adhab A, Altmimi H, Alhawashi M, Alabduliabbar H, Harrathi F, AI mubarek H (2016) Iot for remote elderly patient care based on fuzzy logic, 11–13 May 2106, Yasmine Hammamet, Tunisia
Anousouya Devi M, Ravi S, Vaishnavi J, Punitha S (2016) Classification of cervical cancer using artificial neural networks. Proc Comput Sci 89:465–472
Arsene CTC, Lisbo PJG (2007) Artificial neural networks used in the survival analysis of breast cancer patients: a node- negative study. In: Outcome prediction in cancer, pp 191–239
Aslam MW, Nandi AK (2010) Detection of diabetes using genetic programming. In: 18th Europeon signal processing conference, 23–27 Aug 2010, Aalborg, Denmark
Aswin V, Deepak S (2012) Medical diagnostics using cloud computing with fuzzy logic and uncertainity factors. In: International symposium on cloud and services computing (ISCOS), 17–18 December 2012, Mangalore, India
Belciug S, Gorunescu F (2014) Error-correction learning for artificial neural networks using bayesian paradigm-application to automated medical diagnosis. J Biomed Inform 52:329–337
Bhanot K, Peddoju SK, Bhardwaj T (2019) A model to find optimal percentage of training and testing data for efficient ECG analysis using neural network. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-015-0398-7
Bhuvaneswari P, Therese AB (2015) Detection of cancer in lung with K-NN classification using genetic algorithm. Proc Mater Sci 10:433–440
Billones RKC, Vicmudo MP, Dadios EP (2015) Fuzzy Inference system wireless body area network architecture simulation for health monitoring. In: International conference on technology, communication and control, environment and management (HNICEM), 9–12 December, 2015, Cebu City, Philippines
Broadway M, Kwiatkowska M, Mathews L (2008) A fuzzy logic approach to modeling physical activity levels of obstructive sleep apnea patients. In: Annual meeting of the North American Fuzzy Information Processing Society, 19–22 May 2008, New York City, NY, USA
Brulin D, Benezeth Y, Courtial E (2012) Posture recognition based on fuzzy logic for home monitoring of the elderly. IEEE Trans Inf Technol Biomed 16(5):974–982
Bucinskia A, Baczek T, Krysinskid J, Szoskiewicze R, Zaluskie J (2007) Clinical data analysis using artificial neural networks(ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy. Rep Pract Oncol Radiother 12(1):9–17
Chan KY, Ling SH, Hyugen HT, Jiang F (2012) Hypoglycemic episode diagnosis system based on neural networks for Type 1 diabetes mellitus. In: Evolutionary computation (CEC), 10–15 June 2012
Chatterjee S, Xie O, Dutta K (2012) A predictive modeling engine using neural networks: diabetes management from sensor and activity data. In: 14th international conference on e-health networking, applications and services (HealthCom), pp 230–237, 10–13 Oct 2012, Beijing
Cheng HD, Xu H (2002) A novel fuzzy logic approach to mammogram contrast enhancement. Inf Sci 148(1–4):167–184
Chetty G, Scarvell J, Mitra S (2013) Fuzzy texture descriptors for early diagnosis of osteoarthritis. In: IEEE international conference on fuzzy systems (FUZZ), 7–10 July 2013, Hyderabad, India
Cordeiro FR, Santos WP, Silva-Filho AG (2016) An adaptive semi-supervised fuzzy growcut algorithm to segment masses of regions of interest of mammographic images. Appl Soft Comput 46:613–628
Dalakleidi KV, Zarkogianni K, Karamanos VG, Thanopoulou AC, Nikita KS (2013) A hybrid genetic algorithm for the selection of the critical features for risk prediction of cardiovascular in Type 2 diabetes patients. In: IEEE 13th international conference on bioinformatics and bioengineering (BIBE), 10–13 Nov 2013, Chania, Greece
de Carvalho Filho AO, de Sampaio WB, Silva AC, Paiva AC, Nunes RA, Gattass M (2014) Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif Intell Med 60(3):165–177
Dudek G, Grywna Z, Willcox ML (2008) Classification of antituberculosis herbs for remedial purposes by using fuzzy sets. Biosystems 94(3):285–289
Dutta D, Pradhan A, Acharya OP (2019) IoT based pollution monitoring and health correlation: a case study on smart city. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-019-00802-z
EI-Sappagh S, Elmogy M, Riad AM (2015) A fuzzy-ontology-oriented case based reseaning framework for semantic diabetes diagnosis. Artif Intell Med 65(3):179–208
El-Solh AA, Hsiaao C-B, Goodnough S, Serghani J, Grant BJ (1999) Predicting active pulmonary tuberculosis using an artificial neural network. Chest 116(4):968–973
Fong S, Mohammaed S, F J, Kwoh CK (2013) Using casuality modeling and fuzzy lattice reasoning algorithm for predicting blood glucose. Expert Syst Appl 40(18):7354–7366
Fong S, Wang D, Fiaidhi J, Mohammed S, Chen L, Ling L (2016) Clinical pathways inference from decision rules by hybrid stream mining and fuzzy unordered rule induction strategy. In: Computerised medical imaging and graphics, July2016
Froelich W, Papageorgiou EI, Samarinas M, Skriapas K (2012) Application of evolutionary fuzzy cognitive maps to the long term prediction of prostate cancer. Appl Soft Comput 12(12):3810–3817
Garibaldi JM, Zhou S-M, Wang X-Y, John RI, Ellis IO (2012) Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models. J Biomed Inform 45(3):447–459
Gatton TM, Lee M (2010) Fuzzy logic decision making for an intelligent home healthcare system. In: 5th international conference on future information technology (Future Tech), 21–23 May 2010, Busan, South Korea
Geman O, Chiuchisan L, Toderean R (2017) Application of adaptivde neuro- fuzzy inference system for diabetes classification and prediction. In: E-health and bioengineering conference (EHB), 22–24 June 2017, Sinaia, Romania
Gheyondian N, Nguyen HT, Colagiuri S (2001) A novel fuzzy neural network estimator for predicting hypoglycaemia in insulin- induced subjects. In: Proceedings of the 23rd annual international of the IEEE engineering in medicine and biology society, 25–28 Oct 2001, Istanbul, Turkey
Giabbanelli PJ, Torsney-Weir T, Mago VK (2012) A fuzzy cognitive map of the psychosocial determinants of obesity. Appl Soft Comput 12(12):3711–3724
Groshev A (2016) Recent advances of biochemical analysis: ANN as a tool for earlier cancer detection and treatment. In: Artificial neural network for drug design, delivery and disposition, pp 357–375
Gunasundari S, Janakiraman S, Meenambal S (2016) Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst Appl 56:28–47
Hamdan H, Garibaldi JM (2014) Automatic generation of ANFIS rules in modelling breast cancer survival. In: International conference on computer assisted system in health (CASH), 19–21 Dec 2014, Kuala Lumpur, Malaysia
Hamid S (2016) The opportunities and risks of artificial intelligence in medicine and healthcare. In: SPE communications, summer 2016
Hassanien AE (2007) Fuzzy rough sets hybrid scheme for breast cancer detection. Image Vis Comput 25(2):172–183
Huang M-H, Cheng W-C, Lin C-C, Yung-Hung W (2015) Application of a two stage fuzzy neural network to a prostate cancer prognosis system. Artif Intell Med 63(2):119–133
Johra FT, Shuvo MMH (2016) Detection of breast cancer from histapathology image and classifying benign and malignant state using fuzzy logic. In: 3rd international conference on electrical engineering and information communication technology (ICEEICT), 22–24 Sept 2016, Dhaka, Bangladesh
https://royaljay.com/healthcare/how-artificial-intelligence-will-transform-healthcare/
Knok Z, Avdagic Z, Omanovic S (2015) Hybrid neuro-fuzzy expert system for assessing diabetes risk. In: 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), 25–29 May 2015, Opatija, Croatia
Kennedy J, Ederhart R (1995) Partcle swarm optimization. In: Proceedings of ICNN’95-International conference on neural networks. Perth, WA, Australia, 27 November–1 December 1995
Kuo RJ, Cheng WC, Lien WC, Yang TJ (2015) A medical cost estimation with fuzzy neural network of acute hepatitis patients in emergency room. Comput Methods Programs Biomed 122(1):40–46
Lago MA, Ruperez MJ, Martinez-Martenez F, Monserrat C (2014) Genetic algorithms for estimating the biomechanical behavior of breast tissues. In: IEEE -EMBS international conference on biomedical and health informatics (BHI), 1–4 June 2014, Valencia, Spain
Lai JCY, Leung FHF, Ling SH, Nguyen HT (2013) Hypoglycaemia detection using fuzzy inference system with multi objective double wavelet mutation differential evolution. Appl Soft Comput 13(5):2803–2811
Lai JCY, Leung FHF, Ling SH (2014) Hypoglycaemia detection using fuzzy inference system with intelligent optimizer. Appl Soft Comput 20:54–65
Lee E, Choi C, Lee M, Oh K, Kim P (2016) An approach for predicting disease outbreaks using fuzzy inference among physiological variables. In: 10th international conference on innovative mobile and internet services in ubiquitous computing (IMIS), 6–8 July 2016, Fukuoka, Japan
Liew PL, Lee YC, Lin YC, Lee TS, Lee WJ, Wang W, Chien CW (2007) Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease among obese patients. Dig Liver Dis 39(4):356–362
Ling SH, Nguyen HT (2010) Evolved Fuzzy reasoning Model for hypoglycaemic detection. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), 31 Aug–4Sept 2010, Buenos Aires, Argentina
Ling SSH, Nguyen HT (2011) Genetic—algorithm -based multiple regression with fuzzy inference system for detection of nocturnal hypolycemic episodes. IEEE Trans Inf Technol Biomed 15(2):308–315
Ling SH, Nguyen HT (2012) Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model. Artif Intell Med. 55(3):177–184
Ling SH, Nguyen H, Chan KY (2010) Genetic algorithm based fuzzy multiple regression for the nocturnal Hypoglycaemia detection. In: IEEE congress on evolutionary computation (CEC), 18–23 July 2010, Barcelona, Spain
Lookman Sithic H, Uma Rani R (2015) Fuzzy matrix theory as a knowledge discovery in health care domain. Proc Comput Sci 47:282–291
Lukmanto RB, Irwansyah E (2015) The early detection of diabetes mellitus (DM) using fuzzy hierarchical model. Proc Comput Sci 59:312–319
Madkour MA, Roushdy M (2004) Methodology for medical diagnosis based on fuzzy logic. Egypt Comput Sci J 26(1):1–9
Magalhaes A, Junior B, Duarte AA, Netto MB, Andrade BB (2010) Artificial neural networks and bayesian networks as supporting tools for diagnosis of asymptomatic malaria. In: 12th IEEE international conference on e-health networking applications and services (Healthcom), pp 106–111,1–3 July 2010, Lyon
Mahersia H, Boulehmi H, Hamrouni K (2016) Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: a comparative analysis. Comput Methods Programs Biomed 126:46–62
Mathiyazhagan N, Schechter HB (2014) Soft computing approach for predictive blood glucose management using a fuzzy neural network. In: IEEE conference on norbert wiener in the 21st century (21CW), 24–26 June 2014, Boston, MA, USA
Mediahed H, Istrate D, Boudy J, Dorizzi B (2009) Human activities of daily living recognition using fuzzy logic for elderly home monitoring. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), 20–24 Aug 2009, Jeju Island, South Korea
Mitchell M, Tanyi JA, Hung AY (2010) Automatic segmentation of the prostate cancer treatment planning. In: Ninth international conference on machine learning and applications (ICMLA), 12–14 Dec 2010, Washington DC, USA
Miyahira SA, Araujo E (2008) Fuzzy obesity index for obesity treatment and surgical indication. In: IEEE international conference on fuzzy systems, FUZZ-IEEE, 1–6 June 2008, Hong Kong, China
Nakandala D, Lau HCW (2015) A novel approach to determining change of caloric intake requirement based on fuzzy logic methodology. Knowl Based Syst 36:51–58
Nawarycz T, Pytel K, Drygas W (2013) A fuzzy logic approach to the evaluation of health risks associated with obesity. In: Federated conference on computer science and information systems (FedCSIS), 2013, Poland
Nawarycz T, Pytel K, Drygas W (2013) A survey of fuzzy logic in medical and health technology. In: Federated conference on computer science and information systems (FedCSIS), 8–11 Sept 2013, Poland
Nguyen LB, Nguyen AV, Ling SH, Nguyen HT (2013) Combining genetic algorithm and Levenberg–Marquardt algorithm in training neural network for hypoglycemia detection using EEG signals. In: 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 3–7 July 2013, Osaka, Japan
Nguyen T, Khosravi A, Creighton Douglas, Nahavandi Saeid (2015a) Classification of healthcare data using genetic fuzzy logic system and wavelets. Experts Systems with Applications 42(4):2184–2197
Nguyen T, Nahavandi S, Creighton D, Khosravi A (2015b) Mass spectrometry cancer classification using wavelets and genetic algorithm. FEBS Lett 589(24):3879–3886
Ohri K, Singh H, Sharma A (2016) Fuzzy expert system for diagnosis of breast cancer. In: International conference on wireless communications, signal processing and networking (WiSPNET), 23–25 March 2016, Chennai, India
Omisore MO, Samuel OW, Atajeromavwo EJ (2015) A genetic-neuro fuzzy inferential model for diagnosis of tuberculosis. Appl Comput Inform 13:27–37
Orjuela-Canon D, de Seixas J (2013) Fuzzy-ART neural networks for triage in pleural tuberculosis. In: Pan American health care exchanges (PAHCE, 29 April–4 May 2013, Medellin, Columbia
Orsi T, Araujo E, Simoes R (2014) IEEE international conference on fuzzy systems (FUZZ-IEEE), 6–11 July 2014, Beijing, China
Orsi T, Araujo E, Simões R (2014) Fuzzy chest pain assessment for unstable angina based on braunwald symptomatic and obesity clinical conditions. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), 6–11 July 2014, Beijing, China
Padma T, Balasubramanie P (2011) A fuzzy analytic hierarchy processing decision support system to analyze occupational menace forecasting the spawning of shoulder and neck pain. Expert Syst Appl 38(12):15303–15309
Papageorgiou EI, Subramanian J, Karmegam A, Papandriano N (2015) A risk management model for familial breast cancer: a new application using fuzzy cognitive map method. Comput Methods Programs Biomed 122(2):123–135
Pawlovsky AP, Matsuhashi H (2017) The use of a novel genetic algorithm in component selection for a KNN method for breast cancer prognosis. In: Global medical engineering physics exchanges/pan american health care exchanges (GMEPE/PAHCE), 20–25 March 2017, Tuxtla Gutierrez, Mexico
Rajpurohit J, Sharma TK, A A, Vaishali (2017) Glossary of metaheuristic algorithms. Int J Comput Inf Syst Ind Manag Appl 9:181–205
Reza S, Siti S, Salim SB (2014) Artificial neural networks as speech recognizers for dysarthric speech: identifying the best -performing set of MFCC parameters and studying a speaker-independent approach. Adv Eng Inform 28(1):102–110
Ribeiro AC, Silva DP, Araujo E (2014) Fuzzy breast cancer risk assessment. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), 6–11 July 2014, Beijing, China
Roullier V, Cavaro-Menard C, Calmon G, Aube C (2007) Fuzzy Algorithms: application to adipose tissue quantification on MR images. Biomed Signal Process Control 2(3):239–247
Saha S, Datta S, Konar A (2016) A novel gesture recognition system based on fuzzy logic for healthcare applications. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), 24–29 July 2016, Vancouver, BC, Canada
San PP, Ling SH, Nguyen HT (2012) Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system. In: Engineering in medicine and biology society (EMBC), 28 Aug–01 Sept 2012, San Diego, CA, USA.
Sancar N, Tabrizi SS (2017) Body mass index estimation by using an adaptive neuro fuzzy inference system. Proc Comput Sci 108:2501–2506
Semogan ARC, Gerardo BD, Tanguilig III BT, de Castro JT, Cervantes LF (2011) A rule-based fuzzy diagnostics decision support system tuberculosis. In: 9th international conference on sofytware engineering research, management and applications (SERA), 10–12 Aug 2011, Baltimore, MD.USA
Srivastava S, Pant M, Agarwal N (2016) Psychology of adolescents: a fuzzy logic analysis. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-016-0472-9
Srivastava Shilpa, Pant Millie, Nagar Atulya (2017) Yuva: an e-health model for dealing with psychological issues of adolescents. J Comput Sci 21:150–163
Subramanian J, Karmegam A, Papageorgiou E, Papandrianos N, Vasukie A (2015) An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. Comput Methods Programs Biomed 118(3):280–297
Sung W-T, Chiang Y-C (2012) Improved particle swarm optimization algorithm for android medical care IOT using modified parameters. J Med Syst 36(6):3755–3763
Tahmasebipour K, Houghton S (2014) Disease-gene association using a genetic algorithm. In: IEEE international conference on bioinformatics and bio-engineering (BIBE) 10–12 Nov. 2014, Boca, Raton, FA, USA
Tan TZ, Quek C, Ng GS, Ng EYK (2007) A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Syst Appl 33(3):652–666
Tandon R, Adak S, Kaye JA (2006) Neural networks for longitudinal studies in Alzheimer’s disease. Artif Intell Med 36(3):245–255
Tatri F, Akbarzadeh-T M-R, Sabahi A (2012) Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment. J Biomed Inform 45(6):1021–1034
Tseng M-H, Liao H-C (2009) The genetic algorithm for breast tumor diagnosis—the case of DNA viruses. Appl Soft Comput 9(2):703–710
Tsipiuras MG, Exarchos TP, Fatiadis DI (2008) Automates creation of transparent fuzzy models based on decision trees- application to diabetes diagnosis. In: 30th annual internation conference of the IEEE engineering in medicine and biology society (EMBS2008), 20–25 Aug.2008, Vancouver, BC, Canada
Undre P, Kaur H, Patil P (2015) Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination. In: International conference on pervasive computing (ICPC), 8–10 Jan 2015, Pune, India
Uzoka FME, Osuji J, Obot O (2011) Clinical decision support system (DSS) in the diagnosis of malaria: a case comparison of two soft computing methodologies. Exp Syst Appl 38(3):1533–1537
Varkonyi-Koczy AR, Tusor B, Segatto E (2017) Fuzzy logic supported 3D modeling based orthodontics. In: International symposium on medical measurements and applications (MeMeA), 7–10 May2017, Rochester, MN, USA
Veronezi CC, de Azevedo Simões PW, Dos Santos RL, da Rocha EL, Meláo S, de Mattos MC, Cechinel C (2011) Computational analysis based on artificial neural networks for aiding in diagnosing osteoarthritis of the lumber spine. Rev Bras Ortop 46(2):195–199
Wang C-Y, Tsai J-T, Fang C-H, Lee T-F, Chou J-H (2015) Predicting survival of individual patients with esophageal cancer by adaptive neuro- fuzzy inference system approach. Appl Soft Comput 35:583–590
Yeh W-C, Chang W-W, Chung YY (2009) A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Syst Appl 36(4):8204–8211
Yilmaz A, Ayan K, Adak E (2011) Risk analysis in cancer disease by using fuzzy logic. In: Annual meeting of the North American fuzzy information processing society (NAFIPS), 18–20 March 2011, EI Paso, TX, USA
Yilmaz A, Ari S, Kocabicak U (2016) Risk analysis of lung cancer and effects of stress level on cancer risk through neuro-fuzzy model. Comput Methods Programs Biomed 137:35–46
Yuan B, Herbert J (2012) Fuzzy CARA—a fuzzy based context reasoning system for pervasive healthcare. Proc Comput Sci 10:357–365
Zahlmann G, Kochner B, Ugi I, Schumann D, Liesenfeld B, Wegner A, Obermaier M, Mertz M (2000) Hybrid fuzzy image processing for situation assessment diabetic retinopathy. IEEE Eng Med Biol Mag 19(1):76–83
Zarkogianni K, Mitsis K, Arredeondo M-T, Fico G, Fioravanti A, Nikita KS (2014) Neuro-Fuzzy based glucose prediction model for patients with Type1 diabetes mellitus. In: IEEE EMBS international conference on biomedical and health informatics (BHI), 1–4 June 2014, Valencia, Spain
Zhang M, Adamu B, Lin C-C, Yang P (2011) Gene expression analysis with integrated fuzzy C-means and pathway analysis. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBS, 30 Aug–3 Sept 2011, Boston, MA, USA
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Srivastava, S., Pant, M. & Agarwal, R. Role of AI techniques and deep learning in analyzing the critical health conditions. Int J Syst Assur Eng Manag 11, 350–365 (2020). https://doi.org/10.1007/s13198-019-00863-0
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DOI: https://doi.org/10.1007/s13198-019-00863-0