Machine-Learning in Dementia Informatics Research- TutorsIndia.com

1. MACHINE-LEARNING IN DEMENTIA INFORMATICS RESEARCH An Academic presentationby Dr. Nancy Agens, Head, Technical Operations,TutorsIndia Group www.tutorsindia.com Email:info@tutorsindia.com

2. Today'sDiscussion OUTLINE In Brief Introduction Methods Developing a ML-based Model to Identify Dementia Machine-LearningModels Limitations Conclusion

3. InBrief Machine Learning (ML) is the modern method used to predict, recognize, and to assess the disease correctly without the participation of humankind. ML is emerging rapidly in the field of medicine to diagnose the disease, to visualize the disease, and to examine the transmissionofdisease. Dementia is a chronic disease that affects millions of peopleworldwide. Machine learningis usedin neuro-imagingdata analysisfor the treatment of dementiapatients.

4. Introduction Thediagnosisusuallyincludessetofclinicaltestslikecognitiveassessments,historyof patients, andneuro-imaging. This is a very time-consuming process and also quite expensive. Due to the progress and development in the technologies, to advance in information technology, the field of medical sciences has created huge set of data related to thisdisease. The main objective is to create a model based on machine-learning that can be used to predict Alzheimer’s disease, cognitive impairment, and associated diseases ofdementia.

5. Methods A methodology was used to detect dementia patients who are notdiagnosed. This is done with the help of read-encoded data that are collected regularly in primarycare. The methodology is asfollows A list of Read codes that are related to the disease was gathered and this data was used to detect dementiapatients. The dataset was investigated to explore other Read codes that were allotted to the patients who havedementia. Contd..

6. A division of Read codes was then established that has an associationwith dementiapatients. This division of codes are said to be the read-encoded risk factors associated with dementiapatients. The dataset that was obtained was then used to build a model based on machine- learning to identify dementiapatients. The proposed model was then tested and assessed forperformance. The status of dementia patients that have been predicted by the model was then evaluatedfurther. Contd..

7. Figure 1: Overview ofMethodology

8. Developing a ML-basedModel to IdentifyDementia: A classifier was derived using Machine-learning that was used to characterize dementia patients, and this classifier can also be used to detect all possible cases associated with the disease. The Read codes are used for building a dementia classification model. The dataset guides the classifier derived using machine-learning to distinguish the patients who have dementia and those who arehealthy. These classifiers would be influenced to recognize healthypatients.

9. Machine-LearningModels Support Vector Machine (SVM) is a machine-learning model and is widely used for pattern recognition and diagnosing the problems of dementia fromdata. The Naïve Bayes (NB) classifier is a machine-learning approach that can be used to acquire probabilistic knowledge to categorize unseendata. Random Forest (RF) is an algorithmbased on machine-learning that uses data to construct decision trees (DTs), and organize unseen data by merging each decisiontrees. Contd..

10. Logistic Regression (LR) is an easy machine-learning modelthat has been mostly used in binary classification problems and for early detection of thedisease. There are four criterions that can be used to examine the performance of the machine-learning classification: specificity, accuracy, sensitivity, and area under the curve(AUC). After this preliminary assessment, the model was checked on the primarycare datasetto decide undiagnosed dementia patients. Contd..

11. Figure 2: Number of Potentially Undiagnosed DementiaCases

12. Limitations Dementia is one of the ill health problems that have been a great challenge for the health expertsworldwide. Additionally, the disease affects mostly older people above age of60. This disease is worse that it can even cause damage to brain and reduces the patient’s ability to do theiractivities. Still, the researchis carried on to find a cure for this disease from the past 2decades

13. Conclusion The healthcare practitioner determines the accuracy and efficiency of thediagnosis. As there is a lack of practitioners in some areas, it is even more difficult for thediagnosis of thedisease. Machine-Learning helps in the progress of the analysis of medical data, and automatically make the decision fordiagnosis. The machine-learning field has become very active recently that it uses variety of patient’s data to discover new biomarkers for diagnosis and to improve the diagnosticability.

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