An Overview on Revolutionizing Multi Disease Prediction System: Opportunity through Artificial Intelligence

Abstract

A revolutionary change towards proactive and individualised healthcare management has been sparked by the introduction of machine learning technology in the modern healthcare environment. In order to predict numerous diseases at once, this study presents the numerous Disease Prediction System (MDPS), a novel method that uses cutting-edge machine learning techniques to concurrently predict the likelihood of diabetes (both Type 1 and Type 2), Parkinson\'s disease, and heart diseases and create a thorough health profile for each person, the MDPS incorporates data from a wide variety of sources, such as genetic information, health records, lifestyle factors, and environmental information. diseases. To this end, the system makes use of advanced machine learning models, including ensemble approaches, deep learning, and Bayesian networks. The interface is easy to use, making it easier for both individuals and healthcare professionals to interpret the results. This promotes proactive disease prevention, efficient resource allocation, customised healthcare programmes, and research possibilities to better understand complicated health dynamics. The MDPS is a significant step towards changing healthcare practices as it aims to close the gap between current reactive healthcare models and a future defined by predictive and preventative tactics. With the use of the accurate risk assessments and proactive health monitoring, it promises better patient outcomes and lower healthcare expenditures. Disease prediction and healthcare delivery could undergo even more transformation in the future with improvements in data integration and model development.

Introduction

I. INTRODUCTION

An intelligent and cutting-edge healthcare tool, a multiple disease prediction system makes use of artificial intelligence, data analytics, and machine learning algorithms to forecast a person's risk of contracting multiple diseases before symptoms become apparent. By emphasising prevention and early intervention over just treatment, this approach seeks to change the paradigm of healthcare from one that is reactive to one that is proactive.

The use of cutting-edge technologies has become crucial for preventive and efficient illness management in the ever-changing field of modern healthcare. Conventional healthcare paradigms frequently overlooked important potential for prevention in favour of treating diseases after they manifested.

But a new age in healthcare has been brought about by the development of artificial intelligence (AI) and powerful data analytics, with the Multiple Disease Prediction System emerging as a trailblazing answer. In the past, growing disease burdens, scarce resources, and a largely reactive approach to patient treatment have been problems for healthcare systems. The numerous Disease Prediction System forecasts the likelihood of numerous diseases before clinical signs appear, utilising the potential of data-driven technology to solve these issues. This revolutionary change in healthcare from reactive to proactive emphasises early detection and prevention as the cornerstones of quality medical care. A complex combination of multiple data sources forms the basis of the Multiple Disease Prediction System.

A comprehensive dataset is formed by the convergence of genetic information, lifestyle data, electronic health records, and environmental elements. As we explore the complexities of this ground-breaking system, it becomes clear that the combination of medical knowledge and state-of-the-art technology will be critical to the future of healthcare. At the vanguard of this confluence, ready to transform healthcare procedures, enhance patient outcomes, and usher in a new era when diseases are not only treated but also anticipated and avoided, is the Multiple Disease Prediction System. We will go into greater detail about the elements, features, and possible ramifications of this revolutionary healthcare solution in the pages that follow.

II. OVERVIEW

P. Kathale and S. Thorat proposes that a method for distinguishing between healthy individuals and cancerous patients and detecting malignant tissue. The input mammography image goes through pre-processing to get rid of extraneous parts, fragment the tumour zone using morphological procedures, and highlight the area on the primary mammogram image. The patient is in good health if the mammography image is normal. We use Random Forest (RF) models to distinguish between BC patients and people in good health. The RF's classification accuracy for various patient images is 95%. RF classifier processing takes 6.25 seconds[1].

J. Li et al. proposes the invasion of tumours can be assessed using the pathological diagnostic of diabetes and cancer which also offers crucial information for precise diagnosis and treatment[2]. Cancer proliferation is one of the main signs of the disease, according to statistics, is the proportion of cells in mitosis.

S. H. Manishkumar and P. Saranya emphasizes that before they propagate to lymph nodes and various organs, to eradicate cancer cells within an affected area[3]. The goal of the suggested strategy is to develop a trustworthy way to use mammography imaging to recognise early-stage diabetes and cancer cells. The adaptive Deep Convolution Neural Network (ADCNN) is emphasized in the proposed method to identify cancer signs from mammogram pictures. The algorithm categorises cancer as Ordinary, Benign, or Malignant on the basis of the images.

X. Zhou et al. examines the automated deep learning-based breast cancer prediction. It was suggested and developed to identify benign cancers of the breast form malignant tumours, deploy a novel 19-layer sophisticated convolutional neural network (CNN) model[4]. The model evaluation method used is K-fold Cross Validation in the experiments on the BreaKHis dataset. In terms of accuracy, the suggested 19-layer deep CNN-based classifiers outperformed the industry-standard Support Vector Machine and the cutting-edge model of deep learning GoogLeNet.

A. Ponraj and R. A. Canessane  works on Generative Adversarial Networks (GAN) approach that has been suggested is intended to help in breast cancer detection and diagnosis. Fresh data samples produced by deep learning algorithms known as GANs closely match the initial training data. The two halves of a GAN are a discriminator that trains from this unreliable data and a machine that learns to generate false data[5]. In image processing along with other machine vision approaches, the histogram of oriented gradients (HOG) is employed as an attribute descriptor.

N. Khuriwal and N. Mishra depicts that the Research reveals the way we can use deep learning algorithms to use the UCI Dataset for diagnosing diabetes and cancer. Due to computer vision, image processing, medical diagnosis, and natural language processing have task objective very high, deep learning approaches are almost usually used in these areas[7].

III. DEEP LEARNING ARCHITECTURES

Particularly impressive performance has been demonstrated by convolutional neural networks (CNNs) in a range of applications related to computer vision, including object detection and image categorization. CNNs are used to diagnose diabetes and cancer by training models to examine medical pictures, like as mammograms and histological slides, to find suspicious areas or categorise them as benign or malignant.

A. Mammogram Analysis

The usual screening method for diabetes and cancer is mammography. In order to help radiologists identify and diagnose cancer cells abnormalities on mammograms, deep learning algorithms have been developed. These algorithms can be used to detect malignant tumours, microcalcifications, architectural abnormalities, and other symptoms. Deep learning models can automatically and accurately analyse mammographic pictures to provide assessments, potentially lowering human error and increasing early detection rates.

BV = 1/3πRcc*Rmlo*Hmlo

Where:

• BV = volume
• π = pi
• Rcc = craniocaudal radius
• Rmlo = mediolateral oblique radius
• Hmlo = mediolateral oblique height

B. Histopathological Image Analysis

Another essential step in determining a diabetes and cancer plays a major role in diagnosis is the histopathological evaluation of tissue slides. Digital histopathology pictures can be analysed by deep learning algorithms to identify malignant areas and categorise tumours. These models can pinpoint tumour traits such nuclear atypia, mitotic activity, and tumour grade, enabling pathologists to diagnose and prognostize patients with more accuracy.

C. Multi-Modal Fusion

Multi-Modal Fusion: Increasing the accuracy of diabetes and cancer detection by combining data from many imaging modalities. In order to provide a thorough assessment, deep learning techniques can combine data from mammograms, ultrasonography, magnetic resonance imaging (MRI), and molecular imaging. Fusion of multimodal data can enhance diagnostic performance in terms of sensitivity, specificity, and overall accuracy.

IV. PIPELINE

There are various steps in the algorithm for histopathology image analysis. Here is a general description of the procedure:

1. Image Acquisition: Using a microscope and a digital imaging equipment, create histopathological images of tissue samples, usually taken from biopsy or surgical specimens. The resolution and magnification of the photographs can vary.
2. Preprocessing: Utilise preprocessing methods to improve the histopathology pictures' usability and quality. Noise reduction, colour normalisation, contrast enhancement, and image scaling are common preprocessing techniques.
3. Region of Interest (ROI) Selection: Locate and pick out areas of interest in the histopathological pictures that contain pertinent tissue features, including tumour cells or the stroma that surrounds them. This process streamlines computing and concentrates the study on key areas of interest.
4. Feature Extraction: To gather information for categorization or analysis, extract significant features from the chosen ROIs. Descriptors based on morphology, texture, or intensity are examples of features. Local binary patterns (LBP), the Gray-level Co-occurrence Matrix (GLCM), and wavelet transform are frequently used feature extraction methods. Therefore this plays an important role to feature extract the framework.
5. Classification or Analysis: Analyse the extracted features or apply a classification algorithm on them. Various deep learning or machine learning algorithms can be used, depending on the analysis's particular goals. Examples include Convolutional Neural Networks (CNNs), Random Forest, Support Vector Machines (SVM), and other specialised architectures made for histopathology image processing.
6. Model Training and Validation: Set up training and validation data from the dataset. Utilise the training data to develop the classification or analysis framework, and the validation set to evaluate the effectiveness of the model. For a robust model evaluation, one can use methods like cross-validation.
7. Model Evaluation and Optimization: Utilise Useful assessment metrics, including as precision, recall, accuracy, and F1-score, should be used to assess the trained algorithm's performance. If necessary, change the framework’s design or settings to boost performance.
8. Deployment and Application: The model can be used in real-world applications after being trained and validated. The trained model can be applied to categorise newly discovered histological pictures, make accurate diagnoses, estimate prognosis, evaluate therapy response and more.

V. ARCHITECTURE

In order to ensure a suitable and safe implementation of the technology in a medical setting, it's crucial to emphasise that establishing a reliable multi-disease prediction system requires cooperation with healthcare professionals, domain experts, and adherence to ethical principles. Develop a user interaction for physicians or patients to interact with the prediction system, delivering insights and recommendations. To increase the model's precision and help it adjust to shifting patterns, retrain it with updated data on a regular basis.

VI. RECOMMENDATION

The identification of cancer and diabetes has been transformed by improvements in imaging modalities, biomarkers, and AI algorithms. Early diagnosis rates, individualised treatment plans, and patient outcomes have all been enhanced by these technologies. Healthcare practitioners' abilities have been enhanced by the integration of CAD systems with AI algorithms, enabling more precise and effective diagnoses. The identification of breast cancer will be progressively improved by ongoing research, collaboration, and technology developments, which will ultimately improve patient treatment and outcomes. Therefore the upcoming strategy and the transformation of the diagnosis would merely help the customers or patient to be beneficial in the security which customs the requirements with the specializations.

Conclusion

By enhancing the skills of medical practitioners and raising diagnostic accuracies, deep learning techniques have the potential to revolutionise the identification of diabetes and cancer. Deep learning algorithms have demonstrated encouraging results when applied to histopathological and mammographic images, assisting in early detection and lowering false negatives. To overcome the issues and enable the seamless incorporation of deep learning into routine diabetes and cancer screening and diagnosis, which will ultimately benefit patients all over the world, further research, collaboration, and validation studies are required. The Multiple Disease Prediction System emphasises the synergy between medical expertise and cutting-edge technologies, embodying a holistic approach to healthcare through interdisciplinary collaboration among healthcare practitioners, data scientists, and technology specialists. In the future, the system might be improved by integrating wearable device data seamlessly for real-time health monitoring, incorporating new genetic insights for in-depth analysis, and putting a special emphasis on ethical issues and privacy regulations to uphold compliance and trust. To fully realise the system\'s potential to alter healthcare delivery, optimise resource allocation, and enhance population health outcomes, it is imperative to conduct validation efforts across varied populations and link it with electronic health records. The Multiple illness Prediction System is leading the way in healthcare innovation, offering a tailored and adaptive approach to illness prevention and health management.

References

[1] P. Kathale and S. Thorat, \"Breast Cancer Detection and Classification,\" 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-5, doi: 10.1109/ic-ETITE47903.2020.367. [2] J. Li, L. Qiu, Y. Yang and W. Zhou, \"Deep Learning Techniques for Breast Cancer Mitotic Cell Detection,\" 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 2022, pp. 1-4, doi: 10.1109/ICCWAMTIP56608.2022.10016492. [3] S. H. Manishkumar and P. Saranya, \"Detection and Classification of Breast Cancer from Mammogram Images Using Adaptive Deep Learning Technique,\" 2022 6th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India, 2022, pp. 327-331, doi: 10.1109/ICDCS54290.2022.9780770. [4] X. Zhou et al., \"A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection,\" 2020 7th International Conference on Behavioural and Social Computing (BESC), Bournemouth, United Kingdom, 2020, pp. 1-4, doi: 10.1109/BESC51023.2020.9348322. [5] A. Ponraj and R. A. Canessane, \"Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer Detection and Classification,\" 2023 3rd International Conference on Smart Data Intelligence (ICSMDI), Trichy, India, 2023, pp. 527-532, doi: 10.1109/ICSMDI57622.2023.00099. [6] N. S. Ismail and C. Sovuthy, \"Breast Cancer Detection Based on Deep Learning Technique,\" 2019 International UNIMAS STEM 12th Engineering Conference (EnCon), Kuching, Malaysia, 2019, pp. 89-92, doi: 10.1109/EnCon.2019.8861256. [7] N. Khuriwal and N. Mishra, \"Breast Cancer Diagnosis Using Deep Learning Algorithm,\" 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, 2018, pp. 98-103, doi: 10.1109/ICACCCN.2018.8748777. [8] S. Bhise, S. Gadekar, A. S. Gaur, S. Bepari, D. Kale and S. Aswale, \"Detection of Breast Cancer Using Machine Learning and Deep Learning Methods,\" 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2022, pp. 1-6, doi: 10.1109/ICIEM54221.2022.9853080. [9] A. U. Haq et al., \"3DCNN: Three-Layers Deep Convolutional Neural Network Architecture for Breast Cancer Detection using Clinical Image Data,\" 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 2020, pp. 83-88, doi: 10.1109/ICCWAMTIP51612.2020.9317312. [10] N. B. Nair, T. Singh, A. Thakur and P. Duraisamy, \"Deployment of Breast Cancer Hybrid Net using Deep Learning,\" 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2022, pp. 1-6, doi: 10.1109/ICCCNT54827.2022.9984513. [11] M. B. Khan, P. S. Saha and R. Shahrior, \"Feasible Detection of Breast Cancer Metastasis using a CNN-based Deep Learning Model,\" 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, Bangladesh, 2021, pp. 1-4, doi: 10.1109/ICECIT54077.2021.9641195. [12] A. M. L. Santilli et al., \"Self-Supervised Learning For Detection Of Breast Cancer In Surgical Margins With Limited Data,\" 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 2021, pp. 980-984, doi: 10.1109/ISBI48211.2021.9433829. [13] M. Khumdee, P. Assawaroongsakul, P. Phasukkit and N. Houngkamhang, \"Breast Cancer Detection using IR-UWB with Deep Learning,\" 2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Ayutthaya, Thailand, 2021, pp. 1-4, doi: 10.1109/iSAI-NLP54397.2021.9678158. [14] S. Urabinahatti and D. Jayadevappa, \"Breast Cancer Detection Using Deep Learning Technique,\" 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballar, India, 2023, pp. 1-5, doi: 10.1109/ICDCECE57866.2023.10150859. [15] S. Gupta, A. Panwar, R. Yadav, M. Aeri and M. Manwal, \"Employing Deep Learning Feature Extraction Models with Learning Classifiers to Diagnose Breast Cancer in Medical Images,\" 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-6, doi: 10.1109/DELCON54057.2022.9752856. [16] R. R. Sekar, M. R. Kumar, M. V. Vardhan, K. V. Gopal and C. Jayakkam Allan Tilak, \"Approach to Predict Early Stage of Breast Cancer using Machine Learning,\" 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp. 122-125, doi: 10.1109/ICICCS56967.2023.10142851.

Copyright

Copyright © 2024 Sangeetha Mary B, Dr. Mohammed Mujeer Ulla. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.