Bioinformatics and Medical Applications. Группа авторов

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Bioinformatics and Medical Applications - Группа авторов


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need to mature further.

      1.2.1 Comparative Analysis

      Please refer to Table 1.1 to get a comparative study of the methods and understand the strengths and weakness of each. This helped us immensely in designing our prototype.

      1.2.2 Survey Analysis

      Analyzing the literature, we came to know the scope and limitations of prediction techniques. In present days, heart disease rate has significantly increased and the reason behind deaths in the United States. National Heart, Lung, and Blood Institute states that cardiovascular breakdown is a problem in the typical electrical circuit of the heart and siphoning power.

      Ensembles of classifiers are therefore produced using many techniques such as the use of separate subset of coaching dataset in a sole coaching algorithm, utilizing distinctive coaching on a solitary coaching algorithm or utilizing multiple coaching strategies. We learnt about the various techniques employed in ensemble method like bagging, boosting, stacking, and majority voting and their affect on the performance improvement.

      We also learned about Hoeffding Tree which is the first distributed algorithm for studying decision trees. It incorporates a novel way of dissecting decision trees with vertical parallelism. The development of effective integration methods is an effective research field in AI. Classifier ensembles are by and large more precise than the individual hidden classifiers. This is given the fact that several learning algorithms use local optimization methods that can be traced to local optima.

      A few methodologies find those features by relationship which can help successful predictive results. This used in combination with ensemble techniques achieves best results. Various combinations have been tried and tested and none is the standardized/best approach. Each technique tries to achieve a better accuracy than the previous one and the race continues.

      Machine learning and information gathering utilizes ensembles on one or more learning algorithms to get different arrangement of classifiers with the ability to improve performance. Experimental studies have time and again proven that it is unusual to get one classifier which will perform the best on the general problem domain. Hence, ensemble of classifiers is often produced using any of the subsequent methods.

       • Splitting the data and using various chunks of the training data for single machine learning algorithm.

       • Training one learning algorithm using multiple training parameters.

       • Using multiple learning algorithms.

      Key ideas such as the data setup, data classification, data mining models, and techniques are described below.

Feature name Variable name Value type
Age Age No. of days
Height Height Centimeters
Weight Weight Kilograms
Gender Gender Categories
Systolic blood pressure Ap_hi Integer
Diastolic blood pressure Ap_lo Integer
Cholesterol Cholesterol 1: Standard; 2: Above standard; 3: Well above standard.
Glucose Glu 1: Standard; 2: Above standard; 3: Well above standard.
Smoking Smoke Dual
Alcohol intake Alco Dual
Physical activity Active Dual
Presence or absence of CVDs cardio Dual

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