Sera obtained during the Hajj seasons of 1985, 1986, 1987 and 1988 from 125 heat stroke patients were collected and subjected to chemical analysis which included determination of glucose, lactate, cholesterol, and triglycerides, as well as assay of T3, T4, TSH and cortisol. Hyperglycaemia and lactic acidaemia were found to be the most frequent metabolic abnormalities. The changes in cholesterol did not exhibit a specific pattern. Triglycerides were significantly elevated in only 6% of patients. Cortisol levels were very significantly elevated in precooled patients. Although the mean T3 levels remained within the normal range, the mean concentration on admission was significantly higher than the post-cooling mean. TSH and T4 levels did not show significant changes.

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— Prediction of heart disease is a big concern now a days because everyone is busy and due to heavy load of work people do not give attention to their health. To diagnose a disease is a big challenge. The issue is to extract data that have some meaningful knowledge. For this purpose, data mining techniques are used to extract meaningful data. Decision Tree and ID3 are used to predict heart diseases. Many researchers and practitioners are familiar with prediction of heart diseases and wide range of techniques is available to predict disease. To address this problem, Decision Tree is used to predict the heart disease. In this study the collected data is pre-processed, Decision Tree algorithm and ID3 were then applied to predict the heart disease.   Index Terms— Decision Tree, ID3 Algorithm, Data Mining, Decision Support System (DSS), knowledge Discovery from Databases (KDD).

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